- Email: [email protected]
A new approach for image enhancement applied to low-contrast–low-illumination IC and document images
Pattern Recognition Letters 26 (2005) 769–778 www.elsevier.com/locate/patrec
A new approach for image enhancement applied to low-contrast–low-illumination IC and document images Chung-Chu Leung *, Ka-Shing Chan, Hoi-Mei Chan, Wai-Kin Tsui Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Received 21 July 2003; received in revised form 12 August 2004 Available online 5 November 2004
Abstract Machine vision has become an integral part of todayÕs electronic industry. Digital images capturing low contrast and low illumination conditions often cause serious problems in optical character recognition systems. This paper uses poorly digitized images of integrated circuits and documents to demonstrate the effectiveness of using the Generalized Fuzzy Operator with Histogram Equalization and Partially Overlapped Sub-Block Histogram Equalization in reducing background noise and increasing the readability of text by contrast enhancement. 2004 Elsevier B.V. All rights reserved. Keywords: Contrast enhancement; Generalized fuzzy operator; Document images; Histogram Equalization; POSHE
1. Introduction Machine vision has many important applications in the electronics manufacturing industry, including the evaluation and testing of electronic components, inspection of printed circuit boards (PCB), and pick-and-place assembling and examination along a production line. In order to check the proper placement of components on a printed circuit board or sort out different ICs in a running * Corresponding author. Tel.: +852 28592696; fax: +852 25598738. E-mail addresses: [email protected], [email protected] (C.-C. Leung).
bell line, it is essential for the vision system to identify information markings on the components. These markings are usually in the form of alphanumeric characters or bar-codes which represent the serial or model number of the components. Pattern recognition systems, such as optical character recognition (OCR) software, are used to convert characters in the images to their corresponding ASCII values for subsequent operations. However, poor lighting conditions during image capture can seriously degrade the quality of the digitized image, especially document images. Image scanners and page readers are designed to handle a wide range of documents such as business letters, newspapers, bank checks and credit card
0167-8655/$ - see front matter 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.patrec.2004.09.032
770
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
slips. The amount of text the OCR software can recognize from the document image depends on the quality of the image in terms of contrast and resolution. OCR results are output for postprocessing by application software such as word processors or automated processing systems. In general, document images captured by image scanners are often noisy and low in contrast. Documents digitized by charge coupled device (CCD)/ digital cameras in non-uniform illumination conditions may also lead to poor image quality. Both these image acquisition methods degrade the quality of the digitized image, and directly influence the accuracy of character recognition. Previous studies have used the Multiresolution and Fuzzy Logic Method (Sattar and Tay, 1999), the Human Visual System Method (Heucke et al., 2000), Histogram Equalization (HE) (Gonzalez and Woods, 2002) and Partially Overlapped Sub-Block Histogram Equalization (POSHE) (Kim et al., 2001) for image contrast enhancement. However, the performance of these methods is insufficient for good character recognition using OCR. Leung et al. (2002) presented a modified Generalized Fuzzy Operator (mGFO) method based on the Generalized Fuzzy Operator with Least Squares Approach. It was suggested that local contrast normalization is effective for improving the text readability in low contrast over-bright illumination conditions. However, it does not work on images with low contrast and low illumination. In this paper, we propose a new approach based on the Generalized Fuzzy Operator, pre-processed with HE or POSHE. This method achieves better performance in contrast enhancement of extremely low contrast and low illuminated images. This paper is organized as follows: Section 2 presents the image enhancement using our new approach. In Section 3, a comparison and discussion with three other methods are presented based on real images. A conclusion is given in Section 4.
2. Contrast enhancement of the new approach Our new approach includes three steps: (i) preprocessing, (ii) contrast normalization and (iii) image enhancement.
2.1. Pre-processing Dark background and low contrast in IC and document images are problems that arise due to a poorly illuminated environment during image acquisition. Using mGFO alone, no improvement in image contrast is achieved since no significant difference of gray level in the image. Hence, a pre-processing technique is introduced in our approach to rescale pixels of the image background onto gray levels higher than the objects of interest (e.g. characters). Then, mGFO is used to enhance the objects of interest so that the background can be significantly distinguished from the objects of interest. We have introduced two methods of pre-processing in our approach. First, HE is a simple and effective method of contrast enhancement that is done by stretching the dynamic range of important objects in an image. The histogram of the image is automatically rescaled by a transformation function that rescales pixel values to cover all of the available gray level range. Second, POSHE is performed based on the Sub-Block Histogram Equalization (Kim et al., 2001) which allows pixels in each sub-block to adapt to its neighboring pixels. This method uses partially overlapped sub-blocks, which takes advantage of local adaptability. 2.2. Contrast normalization using least square method (LS) Contrast is the perceived difference in luminance between objects and background, and is defined by WeberÕs Law (Gonzalez and Woods, 2002) as: ci ¼
j Bo Bb j Bb
ð1Þ
where ci is the contrast of the image, Bo is the brightness stimuli of the object, and Bb is the brightness stimuli of the image background. According to Veldkamp and Karssemeijer (2000), noise level is related to the standard error of feature values. It is suggested that rescaling local contrast features with the standard deviation of local contrast, rci , can remove background noise. The normalized local contrast, c0i is defined as follows,
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
c0i ¼
ci lci ðx; yÞ rci ðx; yÞ
ð2Þ
where ci is the local contrast, lci is the mean local contrast, rci is the filtering function and i is the image area. Both lci and rci are signal dependant and varies with the gray level of image pixels. Our approach is related to this definition but our concept of noise is not the same. Since the image of the object depends on the light source, background noise from high intensity illumination areas also depends on the light reflection rate of the object. According to this concept, the image can be divided into three regions in which the lowest light reflection rate is a factor of the objects and the background: The first region (A) is where illumination is greatest but contrast is lowest, the second region (B) has normal illumination and minimum noise, and the darkest region with lowest illumination is the third region (C). These three regions (A, B, and C) are matched with each other using the adaptive Generalized Fuzzy Operator with a Least Square Method (Leung et al., 2002) to achieve the smoothing functions rci and rc2 . The bright region (A), the normal contrast region (B) and the dark region (C) are selected in sequence. The pixel values of region A are converted to a discrete sequence, {Si}, where i is the index in the range [0, n 1] and n is the total number of pixels in the selected region. Similarly, discrete sequences {Sj} and {Sk} (where j = k = 0, 1, . . . , n 1) are created from regions B and C respectively. In each sequence, pixel values are arranged from low to high. To estimate the autoregressive model parameters (Cohen, 1986), the bright/normal error, en1, is defined as the difference between the bright sequence, {Si}, and normal sequence {Sj}; similarly the normal/dark error, en2, is defined as the difference between the normal sequence, {Sj}, and dark sequence, {Sk}, which are given by, en1 ¼ S i S j ¼ S i þ
q X
^ ap S jp
ð3Þ
p¼1
en2 ¼ S j S k ¼ S j þ
q X p¼1
^ bp S kp
ð4Þ
771
where ^ap and ^bp represent the estimated pixel differences between the bright and normal sequences, and between the normal and dark sequences respectively. The Least Square Method is used to minimize the expectation of the squared errors en1 and en2, 8 !2 9 q < = X ð5Þ Min Efe2n1 g ¼ Min E Si þ a^p S jp a^j a^j : ; p¼1
Min Efe2n2 g ¼ Min E b^j
b^j
8 < :
Sj þ
q X p¼1
a^p S kp
!2 9 = ;
ð6Þ
The minimization is performed by differentiating the estimated errors with respect to a1 and b1 respectively, oEfe2n1 g ¼0 oa^1
ð7Þ
and oEfe2n2 g ¼0 ob^1
ð8Þ
The estimated error values from Eqs. (7) and (8) is used as the smoothing function to normalize the local contrast, given by, rc1 ¼ ^a1
ð9Þ
and rc2 ¼ ^b1
ð10Þ
2.3. Image enhancement using the modified Generalized Fuzzy Operator (mGFO) The image enhancement algorithm is based on processing the Generalized Membership Function (GMF) of the selected region within the GFO (Leung et al., 2000, 2003) to give a new fuzzy set. The Generalized fuzzy set (GFS), S, is an extension of the fundamental fuzzy set (Chen et al., 1995), and is described as follows, Definition 1. A Generalized Fuzzy Set (GFS), S, in the region, R, is defined as Z lS ðxÞ S¼ ; x2R ð11Þ x
772
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
here lS(x) 2 [1, 1] is called the Generalized Membership Function (GMF) of S on R. When lS(x) 2 [1, 0), the GMF of x in S is independent on R. When lS(x) 2 (0, 1], the GMF of x in S is dependent on R. When lS(x) = 0, the GMF is the fuzzy bound point function (FBF) in S. If lS(x) = 1 or lS(x) = +1, we can say that x is full independent to R and full dependent to R respectively. If R is finite and only includes finite elements, R = {x1, x2, . . . , xn}, then the GFS, S can be defined by n [ lS ðxi Þ S¼ xi i¼1
ð12Þ
Definition 2. Let pi have some properties in the grades xi, where xi 2 R (i = 1, 2, . . . , N), and pi 2 [1, 1]. Then, the function set, P, consisting of pi is called a Generalized Property Set (GPS) in R. If a 2-D gray image, X = (xij), with an M · N matrix, based on Eq. (12), X can be written as X ¼
M [ N [ pij x i¼1 j¼1 ij
ð13Þ
where pij/xij, 1 6 pij 6 1, denotes the grade, pij, that may have some properties of every element (i, j) in image X. In addition, we also define a Generalized Fuzzy Operator (GFO) as a function of the GFS, lS(x), and then generate another fuzzy set, lS 0 ðxÞ, i.e. lS 0 ðxÞ ¼ GFO½lS ðxÞ
ð14Þ
According to the empirical formula defined by Chen et al. (1995), lS 0 ðxÞ ¼ GFO½lS ðxÞ 8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b b > 1 ½1 þ lS ðxÞ ; > > < ¼ ½lS ðxÞ b ; > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : b b 1 a½1 lS ðxÞ ;
Property 1. When b ! 1, 8 > < 1; 1 6 lS ðxÞ < 0 0 lS ðxÞ ¼ 0; 0 6 lS ðxÞ < r > : 1; r 6 lS ðxÞ 6 1 Property 2. When b > 1, 8 > < lS 0 ðxÞ > lS ðxÞ; if 1 6 lS ðxÞ < 0; r < lS ðxÞ 6 1 > : lS 0 ðxÞ < lS ðxÞ; if 0 < lS ðxÞ 6 r
ð16Þ
ð17Þ
The generalized fuzzy set, S, becomes a normal fuzzy set, S 0 . For our purposes, we will set b = 2 and use the GFO to map all pixels in the original image to the GFS. Now, for the image Xij, where i = 1, 2, . . . , N and j = 1, 2, . . . , M we use a Sine form Generalized Fuzzy Membership Function to transform Xij into a fuzzy set P 0ij : p X ij X min 1 P ij ¼ T ðX ij Þ ¼ sin 2 D
ð18Þ
min 6 D then, Pij 2 [1, 1] is mapped to where X max X 2 the new fuzzy set, P 0 , as follows. 8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 > ð1 ð1 þ P ij ÞÞ ; 1 6 P ij < c > > < P ij ¼ c ð19Þ P 0ij ¼ P 2ij ; > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : 2 ð1 að1 P ij ÞÞ ; c < P ij 6 1
where c is a particular desired gray-level. In essence, we map P to a gray scale interval lower than the desired gray-level if P is in the range [1, c). In the range (c, 1], we map P to a range higher than the desired gray-level. After inverse transformation, we obtain an image, X 0ij . ( " 1 #) sin ðP 0ij Þ 0 X ij ¼ X min þ D 1 ð20Þ p 2
1 6 lS < 0 0 6 lS < r r 6 lS 6 1 ð15Þ
where b can range from 1 to infinity
Here, Eqs. (18)–(20) are called the GFO of the edge detection of an image. Now, based on the GFS described in Eq. (19), a new GFS has been formed which fits onto contrast enhancement. Let Pk 2 [1, 1], k = 1, 2, . . . , 5 and D is obtained experimentally and set to the median of the pixel values in the image. Fuzzy sets are
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
formed for the three different regions in the image. The maximum and minimum pixel values of the bright region are selected as fuzzy sets P1 and P2 respectively. The mean pixel value of the normal contrast region is selected as fuzzy set P3. The maximum and minimum pixel values of the dark region are selected as fuzzy sets P4 and P5 respectively. Each pixel of the original image is mapped onto a fuzzy set P(i, j) according to Eq. (13), which is re-mapped to the new fuzzy set P 0 (i, j) by 8 P ði; jÞ P 3 > ; P 1 < P ði; jÞ < P 2 > > >a rc1 > < P ði; jÞ P 3 P 0 ði; jÞ ¼ ; P 4 < P ði; jÞ < P 5 a > > > rc2 > > : ½P ði; jÞ 2 ; Others ð21Þ The local contrast is normalized by the smoothing functions rc1 and rc2, and the object enhancement is given by [p(i, j)]2. By setting a = 1 and b = 2, the normalized image based on the new fuzzy set, P 0 (i, j), is given by Eq. (20). Since the smoothing functions are dependant on the normal contrast region, we suggest that the normalizing capability and the output pixel values should be limited to the range of input pixel values. 3. Results and discussions The effectiveness and quality of image enhancement is difficult to evaluate. However, since considerable existing literature use edge detection as a method to evaluate image enhancement quality (Schilling and Cosman, 2002; Starck et al., 2003),
773
we have also used edge detection as a means of quantitative assessment of the different image enhancement methods presented in this paper. We used one image of bar codes and digits, and two real images (one IC chip and one document image) to evaluate the effectiveness of our approach against other commonly used image enhancement methods. The image of bar codes and digits (529 · 594 pixels) captured by a digital camera in a uniform low illumination environment is shown in Fig. 1(a). Fig. 1(b)–(e) show the results of using HE alone, POSHE alone, mGFO with HE, and mGFO with POSHE respectively. The contrast enhanced bar codes and digits are clearly legible by eye, but computer programs may have difficulties recognizing the patterns and digits even in the enhanced images. Using the Sobel operator according to the definition of Starck et al. (2003), the percentage of recovered edge pixels of the bar codes and digits are: 0% for the HE and POSHE enhancement methods, 95% for the mGFO with HE, and 66% for the mGFO with POSHE method. This indicates that using the mGFO with HE is most effective at enhancing image quality. Fig. 2(a) and (d) show the results of edge extraction by the edge detector after HE, POSHE, mGFO with HE, and mGFO with POSHE respectively. Enhancement results of a low contrast AMD CPU image captured by a digital camera and scanned using a Hewlett Packard (HP) ScanJect 5200C Scanner are also presented here to compare the recognition of the serial number and characters in the image using the mGFO method pre-processed with HE and POSHE respectively. In this image, the serial number and characters are of
Fig. 1. A simulation document image containing bar codes and digits. (a) Original image; (b) Histogram Equalization; (c) POSHE (sub-block size 30 · 30, step-size = 5); (d) mGFO with HE pre-processing, and (e) mGFO with POSHE pre-processing.
774
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
Fig. 2. Bar codes and digits have been extracted by Sobel operator. (a) Histogram Equalization; (b) POSHE; (c) mGFO with HE preprocessing; and (d) mGFO with POSHE pre-processing.
extremely low contrast, and their gray levels are very close to that of the background. The HP OCR software was used to recognize the serial number and characters in the acquired IC image. In Fig. 3(a), none of the characters in the original image can be recognized by the OCR software. Similarly, no characters could be detected from the images enhanced by the Human Visual System Method and POSHE, as shown in Fig. 3(b) and (d), i.e. no improvement in serial number readability is achieved by either of these two methods. A few characters from the HE processed image are recognized by the OCR system, but there are quite a lot of errors as shown in Fig. 3(c). Inaccuracies of the OCR system are mainly due to distortions in the serial number and characters, which indicate that improvement in readability is not significant. Fig. 3(e) shows the image enhanced by mGFO with HE, where only one number is recognized. Fig. 3(f) shows the image enhanced by mGFO with POSHE, in which most of the serial numbers on the right side are correctly recognized. It shows that pre-processing with POSHE can enhance objects of interest in extremely low contrast images with a non-uniform background. Edge detection resulted in the following percentages of recovered edge pixels in characters and digits of the IC image: 17% for the HE, 30% for POSHE, 42% for the mGFO with HE, and 76% for the mGFO with POSHE, implying that mGFO with POSHE is the most effective contrast enhancement method for the IC image. Finally, a document image acquired by a digital camera is used as a test image to evaluate the dif-
ferent enhancement methods. The original image shows very poor contrast and the text is difficult to read. The OCR result and the histograms of the original image are shown in Fig. 4(a). The OCR results and histograms using the Multiresolution Method, the Human Visual System Method, and the GFO with HE, Histogram Equalization and POSHE are shown in Fig. 4(b)–(f), respectively. The resultant images are first compared by visual inspection. Fig. 4(a) shows that the original document image is low in contrast and has low gray levels. It is very difficult to distinguish the text from the background, and readability is impossible for OCR systems. Fig. 4(b) illustrates that the document image processed using the Multiresolution and Fuzzy Logic Method achieves no improvement in readability, and the contrast of the resultant image is even lower than that of the original image. The result of the Human Visual System in Fig. 4(c) also shows no improvement in text readability, and the image contrast is only slightly improved. In contrast to these negative results, Fig. 4(d) illustrates that using the Modified Generalized Fuzzy Operator with HE pre-processing greatly improves the readability of the text, and characters are clearly distinguished from the background. In addition, the overall contrast of the image is enhanced. In Fig. 4(e), the image enhanced by global Histogram Equalization, contrast is only enhanced in the top-left hand quadrant of the image. Visually, characters in the top-left hand quadrant can be read, but the remaining parts are still illegible. The resultant image after POSHE processing is
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
775
Fig. 3. Image, OCR result and histogram around serial number area. (a) Original Image; (b) Human Visual System Method (window size 5 · 5, b = 0.1); (c) Histogram Equalization; (d) POSHE (sub-block size 128 · 128, step-size = 16); (e) Our method with HE preprocessing; (f) Our method with POSHE pre-processing.
776
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
Fig. 4. (a) Original image, OCR result and histogram. (b) Result by method based on Multiresolution and Fuzzy Logic (r = 0.8, c = 40), OCR result and histogram. (c) Result of Human Visual System (window size 5 · 5, b = 0.1), OCR result and histogram. (d) Result of Generalized Fuzzy Operator with HE, OCR result and histogram. (e) Result of Histogram Equalization, OCR result and histogram. (f) Result of POSHE, OCR result and histogram.
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
shown in Fig. 4(f). The overall contrast is significantly enhanced, but the resultant background is contaminated with noise. The readability of the text is improved, but it is quite difficult to extract the characters from the noisy background. The OCR results are used as an objective measure of the improvement in readability of the document text. Characters marked below the document image are those recognized by the OCR system converted into the document file. As seen in Fig. 4(a), none of the characters in the original document are recognized by the OCR system. In Fig. 4(b) and (c), no result is obtained by the OCR system after image enhancement using the Multiresolution Method and Human Visual System Method respectively. In Fig. 4(d), the result of mGFO with HE pre-processing shows that nearly all of the text in the resultant document image is correctly recognized and converted into the document file, except for two words in the bottom right-hand corner. A problem of the OCR system is that incomplete words at the left and right-hand edges of the image lead to recognition discrepancies. Additional errors of the OCR system is that the character ÔaÕ is recognized as Ô@Õ, and there is also an extra character Ô@Õ in the converted document file. On the other hand, if only HE is used, as shown in Fig. 4(e), only a few words in the top left-hand corner are recognized and converted into the document file. The result of POSHE in Fig. 4(f) shows that the text in the document is inaccurately recognized by the OCR system due to the noisy background of the resultant image. The histogram of each resultant image is used to analyze the amount of contrast enhancement achieved. Three different results are observed from the histogram characteristics. The first result is where pixel values are set to a narrow gray level range from 0 to 80, such as the result of Multiresolution and Fuzzy Logic shown in Fig. 4(b), and the Human Visual System shown in Fig. 4(c). No enhancement in the objects of interest (characters in this case) is achieved using these two methods. The second result is where the pixel values are evenly distributed over the whole gray-level range from 0 to 255, such as the result of HE shown in Fig. 4(e), and POSHE shown in Fig. 4(f). Few objects of interest are enhanced since the pixel values
777
Table 1 Comparison of the percentages of recovered edge pixels in four contrast enhancement methods in three different IC and document images Methods/percentages of recovered edge pixels/examples
Bar codes and digits (%)
AMD CPU (%)
Document image (%)
HE POSHE mGFO with POSHE mGFO with HE
0 0 66 95
52 30 76 42
78 0 0 91
of the characters are close to that of the noise in the background. Finally, the third result is where the pixel values of the image are separated into two regions as seen in Fig. 4(d), using mGFO with HE/ POSHE pre-processing techniques. Some pixels are set to lower gray levels while others are in the higher gray level range producing filtering and enhancement effects. The background noise with high gray scale values can now be easily separated and removed. Also, objects of interest remain in the local range or in the lower gray level range. In this example, the resultant image is separated into two discrete segments in the ranges [0, 25] and [80, 125] respectively. Edge detection resulted in the following percentages of recovered edge pixels in characters of the document image: 85% for the HE, 0% for POSHE, 99% for the mGFO with HE, and 0% for the mGFO with POSHE, again, demonstrating that using the mGFO with HE gives the best image enhancement result. Table 1 summarizes the percentages of recovered edge pixels in each example. It can be concluded that the best image enhancement method is the mGFO with HE pre-processing. 4. Conclusion The method proposed in this paper using the mGFO with either HE or POSHE pre-processing has achieved the best performance over other methods presented in contrast enhancement of extremely low contrast and low illuminated images. The pre-processing steps of HE and POSHE are useful for setting an optimum pixel value range for the input image so that a better mGFO can be obtained. According to the quantitative estimation
778
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
of image enhancement and OCR results, we conclude that the mGFO with HE method attains the most improvement in readability of characters in low contrast and low illuminated images. References Chen, W.F., Lu, X.Q., Chen, J.J., Wu, G.X., 1995. A new algorithm of edge detection for color image: Generalized fuzzy operator. Science in China (Series A) 38 (10), 1272– 1280. Cohen, A., 1986. Biomedical Signal Processing, Vol. 1—Time and Frequency Domains Analysis. CRC Press, Inc., Florida. Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing. Prentice-Hall, USA. Heucke, L., Knaak, M., Orglmeister, R., 2000. A new image segmentation method based on human brightness perception and foveal adaptation. IEEE Signal Process. Lett. 7, 129–131. Kim, J.Y., Kim, L.S., Hwang, S.H., 2001. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans. Circuits Syst. Video Technol. 11, 475–484.
Leung, C.C., Chan, F.H.Y., Kwok, P.C.K., Chen, W.F., 2000. Thyroid cancer cells boundary location by a fuzzy edge detection method. In: ICPR 15th International Conference on Pattern Recognition, Barcelona, Spain, September 3–8. Leung, C.C., Kwok, P.C.K., Chan, F.H.Y., Tsui, W.K., 2002. Normalization of contrast in document images using generalized Fuzzy operator with least square method. In: ICPR 16th International Conference on Pattern Recognition, Quebec City, QC, Canada, August 11–15. Leung, C.C., Chen, W.F., Kwok, P.C.K., Chan, F.H.Y., 2003. Brain tumor boundary detection in MR image with generalized fuzzy operator. In: ICIP 2003 IEEE International Conference on Image Processing, Barcelona, Spain, September 14–17. Sattar, F., Tay, D.B.H., 1999. Enhancement of document images using multiresolution and fuzzy logic techniques. IEEE Signal Process. Lett. 6, 249–252. Schilling, D., Cosman, P.C., 2002. Image quality evaluation based on recognition times for fast image browsing applications. IEEE Trans. Multimedia 4, 320–331. Starck, J.L., Murtagh, F., Candes, E.J., Donoho, D.L., 2003. Gray and color image contrast enhancement by the curvelet transform. IEEE Trans. Image Process. 12, 706–717. Veldkamp, W.J.H., Karssemeijer, N., 2000. Normalization of local contrast in mammograms. IEEE Trans. Med. Imag. 19, 731–738.
A new approach for image enhancement applied to low-contrast–low-illumination IC and document images Chung-Chu Leung *, Ka-Shing Chan, Hoi-Mei Chan, Wai-Kin Tsui Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Received 21 July 2003; received in revised form 12 August 2004 Available online 5 November 2004
Abstract Machine vision has become an integral part of todayÕs electronic industry. Digital images capturing low contrast and low illumination conditions often cause serious problems in optical character recognition systems. This paper uses poorly digitized images of integrated circuits and documents to demonstrate the effectiveness of using the Generalized Fuzzy Operator with Histogram Equalization and Partially Overlapped Sub-Block Histogram Equalization in reducing background noise and increasing the readability of text by contrast enhancement. 2004 Elsevier B.V. All rights reserved. Keywords: Contrast enhancement; Generalized fuzzy operator; Document images; Histogram Equalization; POSHE
1. Introduction Machine vision has many important applications in the electronics manufacturing industry, including the evaluation and testing of electronic components, inspection of printed circuit boards (PCB), and pick-and-place assembling and examination along a production line. In order to check the proper placement of components on a printed circuit board or sort out different ICs in a running * Corresponding author. Tel.: +852 28592696; fax: +852 25598738. E-mail addresses: [email protected], [email protected] (C.-C. Leung).
bell line, it is essential for the vision system to identify information markings on the components. These markings are usually in the form of alphanumeric characters or bar-codes which represent the serial or model number of the components. Pattern recognition systems, such as optical character recognition (OCR) software, are used to convert characters in the images to their corresponding ASCII values for subsequent operations. However, poor lighting conditions during image capture can seriously degrade the quality of the digitized image, especially document images. Image scanners and page readers are designed to handle a wide range of documents such as business letters, newspapers, bank checks and credit card
0167-8655/$ - see front matter 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.patrec.2004.09.032
770
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
slips. The amount of text the OCR software can recognize from the document image depends on the quality of the image in terms of contrast and resolution. OCR results are output for postprocessing by application software such as word processors or automated processing systems. In general, document images captured by image scanners are often noisy and low in contrast. Documents digitized by charge coupled device (CCD)/ digital cameras in non-uniform illumination conditions may also lead to poor image quality. Both these image acquisition methods degrade the quality of the digitized image, and directly influence the accuracy of character recognition. Previous studies have used the Multiresolution and Fuzzy Logic Method (Sattar and Tay, 1999), the Human Visual System Method (Heucke et al., 2000), Histogram Equalization (HE) (Gonzalez and Woods, 2002) and Partially Overlapped Sub-Block Histogram Equalization (POSHE) (Kim et al., 2001) for image contrast enhancement. However, the performance of these methods is insufficient for good character recognition using OCR. Leung et al. (2002) presented a modified Generalized Fuzzy Operator (mGFO) method based on the Generalized Fuzzy Operator with Least Squares Approach. It was suggested that local contrast normalization is effective for improving the text readability in low contrast over-bright illumination conditions. However, it does not work on images with low contrast and low illumination. In this paper, we propose a new approach based on the Generalized Fuzzy Operator, pre-processed with HE or POSHE. This method achieves better performance in contrast enhancement of extremely low contrast and low illuminated images. This paper is organized as follows: Section 2 presents the image enhancement using our new approach. In Section 3, a comparison and discussion with three other methods are presented based on real images. A conclusion is given in Section 4.
2. Contrast enhancement of the new approach Our new approach includes three steps: (i) preprocessing, (ii) contrast normalization and (iii) image enhancement.
2.1. Pre-processing Dark background and low contrast in IC and document images are problems that arise due to a poorly illuminated environment during image acquisition. Using mGFO alone, no improvement in image contrast is achieved since no significant difference of gray level in the image. Hence, a pre-processing technique is introduced in our approach to rescale pixels of the image background onto gray levels higher than the objects of interest (e.g. characters). Then, mGFO is used to enhance the objects of interest so that the background can be significantly distinguished from the objects of interest. We have introduced two methods of pre-processing in our approach. First, HE is a simple and effective method of contrast enhancement that is done by stretching the dynamic range of important objects in an image. The histogram of the image is automatically rescaled by a transformation function that rescales pixel values to cover all of the available gray level range. Second, POSHE is performed based on the Sub-Block Histogram Equalization (Kim et al., 2001) which allows pixels in each sub-block to adapt to its neighboring pixels. This method uses partially overlapped sub-blocks, which takes advantage of local adaptability. 2.2. Contrast normalization using least square method (LS) Contrast is the perceived difference in luminance between objects and background, and is defined by WeberÕs Law (Gonzalez and Woods, 2002) as: ci ¼
j Bo Bb j Bb
ð1Þ
where ci is the contrast of the image, Bo is the brightness stimuli of the object, and Bb is the brightness stimuli of the image background. According to Veldkamp and Karssemeijer (2000), noise level is related to the standard error of feature values. It is suggested that rescaling local contrast features with the standard deviation of local contrast, rci , can remove background noise. The normalized local contrast, c0i is defined as follows,
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
c0i ¼
ci lci ðx; yÞ rci ðx; yÞ
ð2Þ
where ci is the local contrast, lci is the mean local contrast, rci is the filtering function and i is the image area. Both lci and rci are signal dependant and varies with the gray level of image pixels. Our approach is related to this definition but our concept of noise is not the same. Since the image of the object depends on the light source, background noise from high intensity illumination areas also depends on the light reflection rate of the object. According to this concept, the image can be divided into three regions in which the lowest light reflection rate is a factor of the objects and the background: The first region (A) is where illumination is greatest but contrast is lowest, the second region (B) has normal illumination and minimum noise, and the darkest region with lowest illumination is the third region (C). These three regions (A, B, and C) are matched with each other using the adaptive Generalized Fuzzy Operator with a Least Square Method (Leung et al., 2002) to achieve the smoothing functions rci and rc2 . The bright region (A), the normal contrast region (B) and the dark region (C) are selected in sequence. The pixel values of region A are converted to a discrete sequence, {Si}, where i is the index in the range [0, n 1] and n is the total number of pixels in the selected region. Similarly, discrete sequences {Sj} and {Sk} (where j = k = 0, 1, . . . , n 1) are created from regions B and C respectively. In each sequence, pixel values are arranged from low to high. To estimate the autoregressive model parameters (Cohen, 1986), the bright/normal error, en1, is defined as the difference between the bright sequence, {Si}, and normal sequence {Sj}; similarly the normal/dark error, en2, is defined as the difference between the normal sequence, {Sj}, and dark sequence, {Sk}, which are given by, en1 ¼ S i S j ¼ S i þ
q X
^ ap S jp
ð3Þ
p¼1
en2 ¼ S j S k ¼ S j þ
q X p¼1
^ bp S kp
ð4Þ
771
where ^ap and ^bp represent the estimated pixel differences between the bright and normal sequences, and between the normal and dark sequences respectively. The Least Square Method is used to minimize the expectation of the squared errors en1 and en2, 8 !2 9 q < = X ð5Þ Min Efe2n1 g ¼ Min E Si þ a^p S jp a^j a^j : ; p¼1
Min Efe2n2 g ¼ Min E b^j
b^j
8 < :
Sj þ
q X p¼1
a^p S kp
!2 9 = ;
ð6Þ
The minimization is performed by differentiating the estimated errors with respect to a1 and b1 respectively, oEfe2n1 g ¼0 oa^1
ð7Þ
and oEfe2n2 g ¼0 ob^1
ð8Þ
The estimated error values from Eqs. (7) and (8) is used as the smoothing function to normalize the local contrast, given by, rc1 ¼ ^a1
ð9Þ
and rc2 ¼ ^b1
ð10Þ
2.3. Image enhancement using the modified Generalized Fuzzy Operator (mGFO) The image enhancement algorithm is based on processing the Generalized Membership Function (GMF) of the selected region within the GFO (Leung et al., 2000, 2003) to give a new fuzzy set. The Generalized fuzzy set (GFS), S, is an extension of the fundamental fuzzy set (Chen et al., 1995), and is described as follows, Definition 1. A Generalized Fuzzy Set (GFS), S, in the region, R, is defined as Z lS ðxÞ S¼ ; x2R ð11Þ x
772
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
here lS(x) 2 [1, 1] is called the Generalized Membership Function (GMF) of S on R. When lS(x) 2 [1, 0), the GMF of x in S is independent on R. When lS(x) 2 (0, 1], the GMF of x in S is dependent on R. When lS(x) = 0, the GMF is the fuzzy bound point function (FBF) in S. If lS(x) = 1 or lS(x) = +1, we can say that x is full independent to R and full dependent to R respectively. If R is finite and only includes finite elements, R = {x1, x2, . . . , xn}, then the GFS, S can be defined by n [ lS ðxi Þ S¼ xi i¼1
ð12Þ
Definition 2. Let pi have some properties in the grades xi, where xi 2 R (i = 1, 2, . . . , N), and pi 2 [1, 1]. Then, the function set, P, consisting of pi is called a Generalized Property Set (GPS) in R. If a 2-D gray image, X = (xij), with an M · N matrix, based on Eq. (12), X can be written as X ¼
M [ N [ pij x i¼1 j¼1 ij
ð13Þ
where pij/xij, 1 6 pij 6 1, denotes the grade, pij, that may have some properties of every element (i, j) in image X. In addition, we also define a Generalized Fuzzy Operator (GFO) as a function of the GFS, lS(x), and then generate another fuzzy set, lS 0 ðxÞ, i.e. lS 0 ðxÞ ¼ GFO½lS ðxÞ
ð14Þ
According to the empirical formula defined by Chen et al. (1995), lS 0 ðxÞ ¼ GFO½lS ðxÞ 8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b b > 1 ½1 þ lS ðxÞ ; > > < ¼ ½lS ðxÞ b ; > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : b b 1 a½1 lS ðxÞ ;
Property 1. When b ! 1, 8 > < 1; 1 6 lS ðxÞ < 0 0 lS ðxÞ ¼ 0; 0 6 lS ðxÞ < r > : 1; r 6 lS ðxÞ 6 1 Property 2. When b > 1, 8 > < lS 0 ðxÞ > lS ðxÞ; if 1 6 lS ðxÞ < 0; r < lS ðxÞ 6 1 > : lS 0 ðxÞ < lS ðxÞ; if 0 < lS ðxÞ 6 r
ð16Þ
ð17Þ
The generalized fuzzy set, S, becomes a normal fuzzy set, S 0 . For our purposes, we will set b = 2 and use the GFO to map all pixels in the original image to the GFS. Now, for the image Xij, where i = 1, 2, . . . , N and j = 1, 2, . . . , M we use a Sine form Generalized Fuzzy Membership Function to transform Xij into a fuzzy set P 0ij : p X ij X min 1 P ij ¼ T ðX ij Þ ¼ sin 2 D
ð18Þ
min 6 D then, Pij 2 [1, 1] is mapped to where X max X 2 the new fuzzy set, P 0 , as follows. 8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 > ð1 ð1 þ P ij ÞÞ ; 1 6 P ij < c > > < P ij ¼ c ð19Þ P 0ij ¼ P 2ij ; > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : 2 ð1 að1 P ij ÞÞ ; c < P ij 6 1
where c is a particular desired gray-level. In essence, we map P to a gray scale interval lower than the desired gray-level if P is in the range [1, c). In the range (c, 1], we map P to a range higher than the desired gray-level. After inverse transformation, we obtain an image, X 0ij . ( " 1 #) sin ðP 0ij Þ 0 X ij ¼ X min þ D 1 ð20Þ p 2
1 6 lS < 0 0 6 lS < r r 6 lS 6 1 ð15Þ
where b can range from 1 to infinity
Here, Eqs. (18)–(20) are called the GFO of the edge detection of an image. Now, based on the GFS described in Eq. (19), a new GFS has been formed which fits onto contrast enhancement. Let Pk 2 [1, 1], k = 1, 2, . . . , 5 and D is obtained experimentally and set to the median of the pixel values in the image. Fuzzy sets are
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
formed for the three different regions in the image. The maximum and minimum pixel values of the bright region are selected as fuzzy sets P1 and P2 respectively. The mean pixel value of the normal contrast region is selected as fuzzy set P3. The maximum and minimum pixel values of the dark region are selected as fuzzy sets P4 and P5 respectively. Each pixel of the original image is mapped onto a fuzzy set P(i, j) according to Eq. (13), which is re-mapped to the new fuzzy set P 0 (i, j) by 8 P ði; jÞ P 3 > ; P 1 < P ði; jÞ < P 2 > > >a rc1 > < P ði; jÞ P 3 P 0 ði; jÞ ¼ ; P 4 < P ði; jÞ < P 5 a > > > rc2 > > : ½P ði; jÞ 2 ; Others ð21Þ The local contrast is normalized by the smoothing functions rc1 and rc2, and the object enhancement is given by [p(i, j)]2. By setting a = 1 and b = 2, the normalized image based on the new fuzzy set, P 0 (i, j), is given by Eq. (20). Since the smoothing functions are dependant on the normal contrast region, we suggest that the normalizing capability and the output pixel values should be limited to the range of input pixel values. 3. Results and discussions The effectiveness and quality of image enhancement is difficult to evaluate. However, since considerable existing literature use edge detection as a method to evaluate image enhancement quality (Schilling and Cosman, 2002; Starck et al., 2003),
773
we have also used edge detection as a means of quantitative assessment of the different image enhancement methods presented in this paper. We used one image of bar codes and digits, and two real images (one IC chip and one document image) to evaluate the effectiveness of our approach against other commonly used image enhancement methods. The image of bar codes and digits (529 · 594 pixels) captured by a digital camera in a uniform low illumination environment is shown in Fig. 1(a). Fig. 1(b)–(e) show the results of using HE alone, POSHE alone, mGFO with HE, and mGFO with POSHE respectively. The contrast enhanced bar codes and digits are clearly legible by eye, but computer programs may have difficulties recognizing the patterns and digits even in the enhanced images. Using the Sobel operator according to the definition of Starck et al. (2003), the percentage of recovered edge pixels of the bar codes and digits are: 0% for the HE and POSHE enhancement methods, 95% for the mGFO with HE, and 66% for the mGFO with POSHE method. This indicates that using the mGFO with HE is most effective at enhancing image quality. Fig. 2(a) and (d) show the results of edge extraction by the edge detector after HE, POSHE, mGFO with HE, and mGFO with POSHE respectively. Enhancement results of a low contrast AMD CPU image captured by a digital camera and scanned using a Hewlett Packard (HP) ScanJect 5200C Scanner are also presented here to compare the recognition of the serial number and characters in the image using the mGFO method pre-processed with HE and POSHE respectively. In this image, the serial number and characters are of
Fig. 1. A simulation document image containing bar codes and digits. (a) Original image; (b) Histogram Equalization; (c) POSHE (sub-block size 30 · 30, step-size = 5); (d) mGFO with HE pre-processing, and (e) mGFO with POSHE pre-processing.
774
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
Fig. 2. Bar codes and digits have been extracted by Sobel operator. (a) Histogram Equalization; (b) POSHE; (c) mGFO with HE preprocessing; and (d) mGFO with POSHE pre-processing.
extremely low contrast, and their gray levels are very close to that of the background. The HP OCR software was used to recognize the serial number and characters in the acquired IC image. In Fig. 3(a), none of the characters in the original image can be recognized by the OCR software. Similarly, no characters could be detected from the images enhanced by the Human Visual System Method and POSHE, as shown in Fig. 3(b) and (d), i.e. no improvement in serial number readability is achieved by either of these two methods. A few characters from the HE processed image are recognized by the OCR system, but there are quite a lot of errors as shown in Fig. 3(c). Inaccuracies of the OCR system are mainly due to distortions in the serial number and characters, which indicate that improvement in readability is not significant. Fig. 3(e) shows the image enhanced by mGFO with HE, where only one number is recognized. Fig. 3(f) shows the image enhanced by mGFO with POSHE, in which most of the serial numbers on the right side are correctly recognized. It shows that pre-processing with POSHE can enhance objects of interest in extremely low contrast images with a non-uniform background. Edge detection resulted in the following percentages of recovered edge pixels in characters and digits of the IC image: 17% for the HE, 30% for POSHE, 42% for the mGFO with HE, and 76% for the mGFO with POSHE, implying that mGFO with POSHE is the most effective contrast enhancement method for the IC image. Finally, a document image acquired by a digital camera is used as a test image to evaluate the dif-
ferent enhancement methods. The original image shows very poor contrast and the text is difficult to read. The OCR result and the histograms of the original image are shown in Fig. 4(a). The OCR results and histograms using the Multiresolution Method, the Human Visual System Method, and the GFO with HE, Histogram Equalization and POSHE are shown in Fig. 4(b)–(f), respectively. The resultant images are first compared by visual inspection. Fig. 4(a) shows that the original document image is low in contrast and has low gray levels. It is very difficult to distinguish the text from the background, and readability is impossible for OCR systems. Fig. 4(b) illustrates that the document image processed using the Multiresolution and Fuzzy Logic Method achieves no improvement in readability, and the contrast of the resultant image is even lower than that of the original image. The result of the Human Visual System in Fig. 4(c) also shows no improvement in text readability, and the image contrast is only slightly improved. In contrast to these negative results, Fig. 4(d) illustrates that using the Modified Generalized Fuzzy Operator with HE pre-processing greatly improves the readability of the text, and characters are clearly distinguished from the background. In addition, the overall contrast of the image is enhanced. In Fig. 4(e), the image enhanced by global Histogram Equalization, contrast is only enhanced in the top-left hand quadrant of the image. Visually, characters in the top-left hand quadrant can be read, but the remaining parts are still illegible. The resultant image after POSHE processing is
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
775
Fig. 3. Image, OCR result and histogram around serial number area. (a) Original Image; (b) Human Visual System Method (window size 5 · 5, b = 0.1); (c) Histogram Equalization; (d) POSHE (sub-block size 128 · 128, step-size = 16); (e) Our method with HE preprocessing; (f) Our method with POSHE pre-processing.
776
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
Fig. 4. (a) Original image, OCR result and histogram. (b) Result by method based on Multiresolution and Fuzzy Logic (r = 0.8, c = 40), OCR result and histogram. (c) Result of Human Visual System (window size 5 · 5, b = 0.1), OCR result and histogram. (d) Result of Generalized Fuzzy Operator with HE, OCR result and histogram. (e) Result of Histogram Equalization, OCR result and histogram. (f) Result of POSHE, OCR result and histogram.
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
shown in Fig. 4(f). The overall contrast is significantly enhanced, but the resultant background is contaminated with noise. The readability of the text is improved, but it is quite difficult to extract the characters from the noisy background. The OCR results are used as an objective measure of the improvement in readability of the document text. Characters marked below the document image are those recognized by the OCR system converted into the document file. As seen in Fig. 4(a), none of the characters in the original document are recognized by the OCR system. In Fig. 4(b) and (c), no result is obtained by the OCR system after image enhancement using the Multiresolution Method and Human Visual System Method respectively. In Fig. 4(d), the result of mGFO with HE pre-processing shows that nearly all of the text in the resultant document image is correctly recognized and converted into the document file, except for two words in the bottom right-hand corner. A problem of the OCR system is that incomplete words at the left and right-hand edges of the image lead to recognition discrepancies. Additional errors of the OCR system is that the character ÔaÕ is recognized as Ô@Õ, and there is also an extra character Ô@Õ in the converted document file. On the other hand, if only HE is used, as shown in Fig. 4(e), only a few words in the top left-hand corner are recognized and converted into the document file. The result of POSHE in Fig. 4(f) shows that the text in the document is inaccurately recognized by the OCR system due to the noisy background of the resultant image. The histogram of each resultant image is used to analyze the amount of contrast enhancement achieved. Three different results are observed from the histogram characteristics. The first result is where pixel values are set to a narrow gray level range from 0 to 80, such as the result of Multiresolution and Fuzzy Logic shown in Fig. 4(b), and the Human Visual System shown in Fig. 4(c). No enhancement in the objects of interest (characters in this case) is achieved using these two methods. The second result is where the pixel values are evenly distributed over the whole gray-level range from 0 to 255, such as the result of HE shown in Fig. 4(e), and POSHE shown in Fig. 4(f). Few objects of interest are enhanced since the pixel values
777
Table 1 Comparison of the percentages of recovered edge pixels in four contrast enhancement methods in three different IC and document images Methods/percentages of recovered edge pixels/examples
Bar codes and digits (%)
AMD CPU (%)
Document image (%)
HE POSHE mGFO with POSHE mGFO with HE
0 0 66 95
52 30 76 42
78 0 0 91
of the characters are close to that of the noise in the background. Finally, the third result is where the pixel values of the image are separated into two regions as seen in Fig. 4(d), using mGFO with HE/ POSHE pre-processing techniques. Some pixels are set to lower gray levels while others are in the higher gray level range producing filtering and enhancement effects. The background noise with high gray scale values can now be easily separated and removed. Also, objects of interest remain in the local range or in the lower gray level range. In this example, the resultant image is separated into two discrete segments in the ranges [0, 25] and [80, 125] respectively. Edge detection resulted in the following percentages of recovered edge pixels in characters of the document image: 85% for the HE, 0% for POSHE, 99% for the mGFO with HE, and 0% for the mGFO with POSHE, again, demonstrating that using the mGFO with HE gives the best image enhancement result. Table 1 summarizes the percentages of recovered edge pixels in each example. It can be concluded that the best image enhancement method is the mGFO with HE pre-processing. 4. Conclusion The method proposed in this paper using the mGFO with either HE or POSHE pre-processing has achieved the best performance over other methods presented in contrast enhancement of extremely low contrast and low illuminated images. The pre-processing steps of HE and POSHE are useful for setting an optimum pixel value range for the input image so that a better mGFO can be obtained. According to the quantitative estimation
778
C.-C. Leung et al. / Pattern Recognition Letters 26 (2005) 769–778
of image enhancement and OCR results, we conclude that the mGFO with HE method attains the most improvement in readability of characters in low contrast and low illuminated images. References Chen, W.F., Lu, X.Q., Chen, J.J., Wu, G.X., 1995. A new algorithm of edge detection for color image: Generalized fuzzy operator. Science in China (Series A) 38 (10), 1272– 1280. Cohen, A., 1986. Biomedical Signal Processing, Vol. 1—Time and Frequency Domains Analysis. CRC Press, Inc., Florida. Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing. Prentice-Hall, USA. Heucke, L., Knaak, M., Orglmeister, R., 2000. A new image segmentation method based on human brightness perception and foveal adaptation. IEEE Signal Process. Lett. 7, 129–131. Kim, J.Y., Kim, L.S., Hwang, S.H., 2001. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans. Circuits Syst. Video Technol. 11, 475–484.
Leung, C.C., Chan, F.H.Y., Kwok, P.C.K., Chen, W.F., 2000. Thyroid cancer cells boundary location by a fuzzy edge detection method. In: ICPR 15th International Conference on Pattern Recognition, Barcelona, Spain, September 3–8. Leung, C.C., Kwok, P.C.K., Chan, F.H.Y., Tsui, W.K., 2002. Normalization of contrast in document images using generalized Fuzzy operator with least square method. In: ICPR 16th International Conference on Pattern Recognition, Quebec City, QC, Canada, August 11–15. Leung, C.C., Chen, W.F., Kwok, P.C.K., Chan, F.H.Y., 2003. Brain tumor boundary detection in MR image with generalized fuzzy operator. In: ICIP 2003 IEEE International Conference on Image Processing, Barcelona, Spain, September 14–17. Sattar, F., Tay, D.B.H., 1999. Enhancement of document images using multiresolution and fuzzy logic techniques. IEEE Signal Process. Lett. 6, 249–252. Schilling, D., Cosman, P.C., 2002. Image quality evaluation based on recognition times for fast image browsing applications. IEEE Trans. Multimedia 4, 320–331. Starck, J.L., Murtagh, F., Candes, E.J., Donoho, D.L., 2003. Gray and color image contrast enhancement by the curvelet transform. IEEE Trans. Image Process. 12, 706–717. Veldkamp, W.J.H., Karssemeijer, N., 2000. Normalization of local contrast in mammograms. IEEE Trans. Med. Imag. 19, 731–738.