A robust infrared dim target detection method based on template filtering and saliency extraction

Infrared Physics & Technology 73 (2015) 19–28

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Infrared Physics & Technology journal homepage: www.elsevier.com/locate/infrared

A robust infrared dim target detection method based on template filtering and saliency extraction Wenguang Wang, Chenming Li ⇑, Jianing Shi School of Electronic and Information Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China

h i g h l i g h t s
Dim target detection is converted to salient region extraction.
Template filtering results and original image are combined to highlight the target.
We study the reason of false alarm in weighted gray map and target saliency map.
The correlation of targets’ position in two maps is used to eliminate false alarms.
The new method includes filtering, feature extraction and position discrimination.

a r t i c l e

i n f o

Article history: Received 13 April 2015 Available online 24 August 2015 Keywords: Infrared dim target detection Template filtering Salient region extraction Position discrimination

a b s t r a c t Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction Infrared dim target detection plays an important role in civilian and military fields such as forest fire prevention, satellite remote sensing, infrared early warning, and precise guidance. Generally, the target is fairly small and lack of texture information, as it is too far from the infrared sensor. In addition, the clutter disturbance in background is so strong and complex that the infrared images with dim target may show the characteristics such as the low SCR (signal-clutter ratio) targets, fuzzy edge between target and background. Therefore, dim target detection in infrared images remains rather difficult and challenging [1,2]. In recent years, dim target detection, recognition and tracking have been studied widely and deeply in infrared images, and lots ⇑ Corresponding author. E-mail address: [email protected] (C. Li). http://dx.doi.org/10.1016/j.infrared.2015.08.015 1350-4495/Ó 2015 Elsevier B.V. All rights reserved.

of methods have emerged. In summary, the dim target detection utilizes the difference such as the gray value and brightness distribution between target and background. The existing methods can be broadly divided into the following two categories: the filtering algorithm and the feature extraction algorithm. In the filtering algorithm, background is removed in the space domain or frequency domain before detecting, such as: morphological filtering [3,4], two-dimensional empirical mode decomposition [5,6], adaptive Butterworth high pass filtering (BHPF) [7], kernel-based nonparametric regression [8], and so on. Generally speaking, these methods have been widely used because of less computing and good performance for target detection with high SCR. The key step is to remove the background from the original image effectively. When the background is very complex or the target has poor SCR, the filtering algorithm has a serious deterioration in detection performance. This kind of method may cause a large number of false alarms under the interference of complex background, for

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W. Wang et al. / Infrared Physics & Technology 73 (2015) 19–28

Fig. 1. Schematic of the proposed method.

the difficulties in removing background. In the feature extraction algorithm, different features extracted both from target and background can be used to distinguish targets from background, such as: local contrast method [9], sparse matrix and low-rank matrix decomposition method [10], and principal curvature method [11]. The detection performance of this kind of algorithm depends on the feature selection and the type of background largely. Therefore, the feature extraction algorithm has poor robustness. The problem of high false alarm rate is a challenge for low SCR target detection in infrared. In order to reduce the false alarms, at the same time improve the adaptability and robustness of dim target detection, a new infrared dim target detection method is proposed by combining the filtering algorithm and the feature extraction algorithm that based on template filtering and saliency extraction. This method is derived by the following reasons: firstly, the gray value of a target is usually higher than that of its immediate background in infrared image and is not spatial correlation with its local neighbors [9], so the target can be deemed as a bright spot. Secondly, dim target could attract attention easily, and we could regard targets as salient regions in infrared image. Therefore, dim target detection can be converted to the salient region extraction. Thirdly, because of the different location of false alarms caused by template filtering and the saliency extraction, the correlation of targets’ position can be used to eliminate false alarms. The rest of this paper is organized as follows: in Section 2, the proposed infrared dim target detection method is described in detail. The results of experiment are analyzed in Section 3. Section 4 makes a conclusion to this work.

2. The proposed method The existing methods do not play well in some fields such as: false alarms elimination, adaptability and robustness for target detection in different backgrounds, because of low SCR of target, fewer pixels, lack of texture information, diverse in background texture, and so on. In order to overcome the drawbacks of the existing methods, a novel method of dim target detection in different backgrounds is proposed to reduce false alarms while keeping a stable detection rate. In addition, this method outperforms several existing methods in adaptability and robustness. The process of this method includes five steps, whose flow chart is presented in Fig. 1.

Step 1: Template filtering is implemented to remove the background and generate a new image with targets. Step 2: Combine the original image with the template filtering results to calculate the weighted gray map (WGM), which increases the target’s SCR. Step 3: Target saliency map (TSM) is obtained from the template filtering results by a saliency detection method called phase spectrum of Fourier Transform (PFT). Step 4: Extract candidate targets from WGM and TSM respectively. Step 5: The correlation of targets’ position is used to eliminate the false alarms from candidate targets and generate the final result. 2.1. Template filtering Infrared dim target is immersed in noise and background, but its brightness is higher than the immediate background; and the target is discontinuity with its neighbors. Fig. 2 shows three kinds of typical infrared backgrounds including the cloud (Fig. 2(a)), the mountain-vegetation (Fig. 2(b)), and the sea-sky (Fig. 2(c)). Because of the long distance between target and infrared sensor, the target can be deemed as an isotropic bright spot in a homogeneous region [12]. The template (Eq. (1)) is implemented to filter the original infrared image, where C is a coefficient. gc(x, y) is obtained by the convolution of template and original image (Eq. (2)).

3 1 1 1 1 1 7 6 0 0 1 7 6 1 0 7 6 7 T ¼ C6 6 1 0 16 0 1 7 7 6 0 0 1 5 4 1 0 1 1 1 1 1

ð1Þ

g c ðx; yÞ ¼ T  Iðx; yÞ

ð2Þ

2

where I(x, y) is the original infrared image; gc(x, y) is the convolution result; the symbol ⁄ represents the convolution operator. If a point lies in a homogeneous background, its value in gc(x, y) will tend to 0. If a point lies in target region, its value in gc(x, y) will be greater than 0 because the gray value of target point is higher than that in background. The greater the difference between a

Fig. 2. Three kinds of typical infrared background.

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Fig. 3. The original image and the saliency map. (a1) and (b1) The original image, (a2) and (b2) The normalized saliency map.

Those points with g c ðx; yÞ 6 0 which represent the background are eliminated, and g(x, y) is the final output from template filtering. In g(x, y), the background around the local maximum point is further suppressed. 2.2. Weighted gray map calculation As we can see, there are local maximum points in g(x, y) including targets. The value of target in g(x, y) is usually large. In order to remove the false alarms and improve the robustness, the weighted gray map (WGM) can be constructed by combining the g(x, y) with the original infrared image as Eq. (4).

WIðx; yÞ ¼ gðx; yÞIðx; yÞ Fig. 4. The sliding window for local detection.

point and background of gray value is, the larger the point value in gc(x, y) is. Template filtering can pick out the local maximum points from the image by the difference in shape and brightness between target and background. Essentially, template filtering is a high-pass filter, and it can reserve the targets while removing the background. Compare gc(x, y) with a threshold as Eq. (3).



gðx; yÞ ¼

g c ðx; yÞ g c ðx; yÞ > 0 0

g c ðx; yÞ 6 0

ð3Þ

ð4Þ

In weighted gray map, the target’s SCR is increased and the target becomes more significant. The points with high value in weighted gray map are to match local maximum and high bright points in the original image. There are much fewer false alarms in WGM than in g(x, y), while false alarms caused by the noise and steep edge of the complex background still exist, though. 2.3. Target saliency map calculation In the infrared image, the dim target can attract more attention of human eyes, because of the difference of shape and brightness between the target and its immediate background. For example, in Fig. 2, we can find out the targets easily. The region where dim target is in is the ‘‘salient region” in the image. Given all this,

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Fig. 5. Infrared sky-cloud image with 3 targets and the processing results. (a) The original image, (b) the normalized weighted gray map, (c) the normalized target saliency map, (d) the detection results from weighted gray map, (e) the detection results from target saliency map, (f) the final detection result.

Fig. 6. Infrared cloud image with single target and the processing results. (a) The original image, (b) the normalized weighted gray map, (c) the normalized target saliency map, (d) the detection results from weighted gray map, (e) the detection results from target saliency map, (f) the final detection result.

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Fig. 7. Infrared mountain-vegetation image and the processing results. (a) The original image, (b) the normalized weighted gray map, (c) the normalized target saliency map, (d) the detection results from weighted gray map, (e) the detection results from target saliency map, (f) the final detection result.

the dim target detection could be converted to saliency extraction in infrared image. Recently, phase spectrum of Fourier Transform (PFT) has been employed to extract the saliency region, and produces satisfied results [13–15]. The procedures of computing saliency map using PFT are presented as follows: Step 1: Calculate frequency spectrum of the original image as Eq. (5).

f ðu; v Þ ¼

M1 N1 XX

Iðx; yÞej2pðux=Mþv y=NÞ

ð5Þ

x¼0 y¼0

where I(x, y) is the original infrared image, and its size is M  N; f (u, v) is the Fourier Transform result of the original image I(x, y). Step 2: Obtain phase spectrum of the infrared image from f(u, v) as Eq. (6).

pðu; v Þ ¼ P½f ðu; v Þ

ð6Þ

where P[] represents the phase spectrum of Fourier Transform. Step 3: Calculate the saliency map as Eq. (7).

Sðx; yÞ ¼ Gðx; yÞ  jF 1 ½expðj  pðu; v ÞÞj2 1

ð7Þ

where F [] denotes the inverse Fourier Transform; G(x, y) is a 2D Gaussian filter; S(x, y) is the saliency map. For the purpose of illustrating the feasibility of transforming from dim target detection to saliency extraction, an example is introduced in Fig. 3. There are 5 regions in Fig. 3(a1), and the gray values of each region are 50, 150, 100, 200, and 30. In addition, the whole image is polluted by Gaussian noise (standard deviation r = 3). As no target in Fig. 3(a1), the saliency map (Fig. 3(a2)) is disorderly, and we are not able to extract saliency region from it. Fig. 3

(b1) is obtained from Fig. 3(a1) by adding four ‘‘targets”, the gray values of them are 150, 180, 120, and 20. In Fig. 3(b2), the saliency map of Fig. 3(b1), we could find four saliency regions easily. In Fig. 3(b1), the target ‘‘4” is the most significant, target ‘‘1”, the bottom half of target ‘‘2” and target ‘‘3” are less significant than target ‘‘4”. In Fig. 3(b2), the gray value of target ‘‘4” is the highest, and the gray values of target ‘‘1” and the bottom half of target ‘‘2” and target ‘‘3” are lower than target ‘‘4”. Fig. 3 also indicates that the noise and steep edge in the image have almost no effect on extracting saliency region by the PFT method. We can also find out two drawbacks of PFT: extra false alarms and shape distortions. (1) Extra false alarms: due to the impact of PFT, there are additional mirror targets (see in Fig. 3(b2)). Moreover, target ‘‘3” and target ‘‘4” are darker than their neighbors, they are not real targets in infrared image; but they are still extracted as saliency region (Fig. 3(b2)). Both scenarios can cause false alarms. (2) Shape distortions: the saliency regions in Fig. 3(b2) show shape distortions especially for the target ‘‘2” and target ‘‘3”. This can cause position offset when detecting target. The background is removed in g(x, y), and the SCR of targets in g (x, y) are higher than them in I(x, y). In this paper, we use g(x, y) instead of I(x, y) in Eq. (5), and the standard deviation of the Gaussian filter in Eq. (7) is 2. Besides, we denote S(x, y) in Eq. (7) as the target saliency map (TSM). As we can see above, noise and steep edges may cause few false alarms in weighted gray map while almost no false alarms caused by noise and steep edges in target saliency map because the saliency points are insensitive to both noise and steep edges. Moreover, PFT method may cause false alarms and shape distortions in TSM. For false alarms, the probability of them that both in weighted gray and target saliency map having the same position

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Fig. 8. Infrared sea-sky image with target in the sky and the processing results. (a) The original image, (b) the normalized weighted gray map, (c) the normalized target saliency map, (d) the detection results from weighted gray map, (e) the detection results from target saliency map, (f) the final detection result.

is extremely low, so we can remove them in Section 2.4. Meanwhile, as the position detection accuracy of targets in weighted gray map is high, the influence of the position offset in target saliency map also can be eliminated in Section 2.4.

of candidate targets detection, the points with R(x, y) = 1 are called candidate targets. k is a coefficient, and the larger k is, the lower the probability that the background judged as target will be. In order to detect the dim target and keep the candidate targets as less as possible at the same time, k usually belongs to [2,4].

2.4. Target detection After calculating the weighted gray map and target saliency map, the following procedure of target detection can be divided into two steps: Step 1: Candidate targets detection. Detect targets both in weighted gray map and target saliency map simultaneously, and the detection results are called candidate targets; Step 2: Position discrimination. Distinguish the target from the candidate targets using the correlation of targets’ position. 2.4.1. Candidate targets detection The sliding window (see Fig. 4) is used to detect candidate targets in both weighted gray map and target saliency map simultaneously. As shown in Fig. 4, the sliding window can be divided into nine cells with the same size. The central cell denoted by ‘‘9” is called cell under test (CUT) where the target could appear, and other cells are called background cell. As the target to be detected is small, the size of the cell can be set as 3  3 or 5  5. The formula of detecting candidate targets is defined as Eq. (8).

 Rðx; yÞ ¼

1 Mt > M b þ kStdb 0 M t 6 Mb þ kStdb

ð8Þ

where Mt is the mean of CUT; and Mb is the mean of background cell; Stdb is the standard deviation of background cell; R(x, y) is the result

2.4.2. Position discrimination Known from the analysis above, the false alarms are located at the local maximum points caused by noise and steep edge of the background in weighted gray map, while in target saliency map the false alarms are located at the saliency regions caused by some dark points and mirror points. The correlation of targets’ position could be used to distinguish the targets, as the positions of false alarms in WGM are different from those in TSM. Position discrimination is divided into four steps: Step 1: Get one candidate target’s position Xi (i = 1, 2 . . . n, n is the number of the candidate targets in WGM) in weighted gray map; Step 2: Calculate the Euclidean distance between Xi and Xj as Eq. (9);

disij ¼ kXi  Xj k2

ð9Þ

where Xj is the jth candidate target position in target saliency map (j = 1, 2, . . . m, m is the total number of the candidate targets in TSM); kk2 denotes 2-norm. Step 3: The minimum distance disimin can be obtained by using Eq. (10);

disi min ¼ minðdisi Þ

ð10Þ

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Fig. 9. Infrared sea-sky image with target in the sea and the processing results. (a) The original image, (b) the normalized weighted gray map, (c) the normalized target saliency map, (d) the detection results from weighted gray map, (e) the detection results from target saliency map, (f) the final detection result.

where min () is to choose the minimum value from distance vector disi (disi = [disi1, disi2, . . ., disij, . . ., disim]); Step 4: Compare disi min with threshold disth. If the minimum distance disi min is larger than disth, the candidate target at position Xi is a false alarm, otherwise, it is a real target. After position discrimination, the false alarms caused by mirror targets, dark points, noise, and steep edges can be removed, because they have weak location correlation. By now, the final

detection result is obtained. The targets positions are determined just by their locations in WGM rather than TSM, so that the effect on target’s position offset in TSM can be minimized. 3. Experiments and results In order to validate the effectiveness of the method proposed in this paper, the measured images under different scenes and simulated images are used to examine the performance.

Fig. 10. Infrared bank-lake image and the processing results. (a) The original image, (b) the normalized weighted gray map, (c) the normalized target saliency map, (d) the detection results from weighted gray map, (e) the detection results from target saliency map, (f) the final detection result.

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Fig. 11. Infrared complex sea clutter image with target in the sky and the processing results. (a) The original image, (b) the normalized weighted gray map, (c) the normalized target saliency map, (d) the detection results from weighted gray map, (e) the detection results from target saliency map, (f) the final detection result.

3.1. Experiments on measured images To validate the effectiveness and robustness of the new method, we use a set of collected real infrared images with complex background as the test images. The images are shown in Figs. 5–11, which covers typical backgrounds including the sky-cloud with multiple targets (Fig. 5(a)), the complex cloud with single target (Fig. 6(a)), the mountain-vegetation (Fig. 7(a)), the sea-sky with target in the sky (Fig. 8(a)), the sea-sky with target in the sea (Fig. 9(a)), the bank-lake (Fig. 10(a)), and the complex sea clutter (Fig. 11(a)). In this experiment, the scenes with strong noise (Figs. 6 (a), 8(a)), the low SCR targets (Fig. 5(a)), the target-like region (Fig. 11(a)) are included as well. There are 9 targets, which are in red rectangles and labeled from 1 to 9, in this experiment. The intermediate steps of the proposed method for different backgrounds images are shown in Figs. 5–11. As shown in Figs. 5–11, (a) are the original images. (b) are the normalized weighted gray maps, and most of the background is removed. (c) are the normalized target saliency maps, and targets become obvious. (d) are the detection results from weighted gray maps, most of the (d) images have a good performance that only the target is detected. But there are still false alarms in few (d) images, such as Figs. 5(d) and 6(d). (e) are the detection results from target saliency maps. Fig. 5(e) has more false alarms than others, because there are more targets and salient regions in Fig. 5(a). Meanwhile, Fig. 11(e) also has some false alarms, the reason is that there are some target-like regions in Fig. 11(a), and the target-like regions are regarded as saliency regions. The more saliency regions are, the more false alarms are. (f) are the final detection results. There

Table 1 The NCDT/NIDT of the three methods.

Top-hat Local contrast method Our method

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

2/0 1/0

1/0 1/0

1/0 1/1

1/0 1/0

1/0 1/0

1/0 1/0

1/0 1/0

3/0

1/0

1/0

1/0

1/0

1/0

1/0

are few false alarms in (d) and (e) though, in the final detection images, we remove the false alarms, and obtain the correct detection results by the correlation of targets’ position. As the new method combines the filtering with the feature extraction, top-hat filter method which represents the filtering algorithm and local contrast method [9], which is one of the feature extraction algorithm are employed to compare with this method. The detection results are in Table 1. NCDT is the number of correctly detected targets, NIDT is the number of incorrectly detected targets. From Table 1, we could see that the new method can detect all 9 targets and 0 false alarms in 7 typical backgrounds, while top-hat and local contrast method are not able to detect all targets. This experiment validates that the new method has better performance on effectiveness and robustness for detecting dim targets in different backgrounds. To compare the performance of these methods quantitatively, signal clutter ratio gain (SCRG) is employed. The SCRG is a common metric used in the evaluation of clutter removal filters. It is a

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W. Wang et al. / Infrared Physics & Technology 73 (2015) 19–28 Table 2 The SCRGs of the filtering methods.

Top-hat The processing to WGM

Target1

Target 2

Target 3

Target 4

Target 5

Target 6

Target 7

Target 8

Target 9

3.3 5.8

1.7 2.2

1.3 1.7

6.5 7.3

1.6 3.7

3.1 3.7

4.8 10.7

1.2 5.2

0.7 2.4

Table 3 The SCRG of the feature extraction methods.

Local contrast method The processing to TSM

Target1

Target 2

Target 3

Target 4

Target 5

Target 6

Target 7

Target 8

Target 9

1.7 2.0

0.8 1.3

1.1 1.5

0.9 6.8

2.2 1.7

1.4 1.8

1.5 2.3

0.9 0.6

0.6 0.8

measure of the improvement in the SCR after filtering. The SCR itself is intended to provide a measure of the strength of signal relative to the clutter [11,16,17].

SCRG ¼ SCR ¼

SCRout SCRin

bI t  Ib

rb

ð11Þ ð12Þ

where bI t denotes the maximum signal intensity; Ib and rb denote the average intensity and standard deviation of the pixels in the neighbor area around the target. The processing to weighted gray map (WGM) belongs to filtering algorithm, and it can be compared with the top-hat method. The processing to target saliency map (TSM) belongs to feature extraction algorithm, and it can be compared with local contrast method. The SCRGs are shown in Tables 2 and 3. Table 2 shows that the SCRGs based on WGM processing are higher than those of top-hat method to the given 7 different backgrounds. It is shown that the processing of weighted gray map can obtain good performance on improving SCR. Table 3 shows that the SCRGs based on the feature extraction algorithm are not always greater than 0. It indicates that the feature extraction algorithm detects targets by recognizing the features of targets rather than simply improving the SCR. The feature extraction introduced to the new method is to eliminate the false alarms in target saliency terms by position discrimination.

Fig. 12. One of the simulated images with SCR = 2.5.

3.2. Experiments on simulated images Real images can be used for verifying the performance of a new method, but they are just special cases. To study the performance of the new method on detecting low SCR targets, the experiment based on simulated infrared images are done. In these experiments, a simulated image is generated as follows: (1) Generate an original image using the Random Midpoint Displacement (RMD) method [1,18]. (2) Obtain the background by adding Gaussian noise to the original image (rN = 2). (3) Obtain the simulated image by adding a target in a random location, and adjusting the target gray value to meet the set SCR. To study the detection performance under different SCR, 3 sets of images with the SCR 2, 2.5, and 3 are simulated respectively. Each set of images includes 100 images. The one of the simulated images with SCR = 2.5 is shown in Fig. 12 (The target is in the red rectangle). The ROC (Receiver Operating Characteristic) curves of several methods including top-hat method, facet model method [19], local contrast method, and TDLMS filter method [20] are shown in Fig. 13. Pd represents the probability of detection rate (Eq. (13)); and Pf represents the probability of false alarm rate (Eq. (14)).

NCDT  100% NT NIDT Pf ¼  100% NP

Pd ¼

ð13Þ ð14Þ

where NT is the number of the targets; NP is the total pixels number in single image. In Fig. 13(a), the Pd of the proposed method obtains more than 90% at the Pf = 0.005, while the other methods only obtain less than 75%. In Fig. 13(b), the Pd of our method exceeds 94% at the Pf = 0.005, in contrast, the Pd of other methods only obtain less than 80%. In Fig. 13(c), the four compared methods fall behind the new method obviously. The new method displays better target detection performance than other four methods by the ROC curves, which is mainly depended on the fact that the false alarms are not located at the same positions in weighted gray map and the target saliency map, and position discrimination in Section 2.4.2 has the character of false alarms suppression. Fig. 13(c) shows that when pd = 53%, the new method has the lowest pf of 0.016%. Fig. 13(b) shows that when pf = 0.116%, the pd of the proposed method is 77%, in contrast, other methods have much lower pd. This experiment shows the proposed method has better false alarms suppression than other four methods to low SCR images while keeping a stable detection rate. But the computing of the proposed method is more than top-hat filter, facet model method, TDLMS method, and only less than local contrast method. Consuming more time is a common drawback for feature extraction algorithm, and it can be improved by parallel computing.

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map is obtained from the infrared image to highlight the target, which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then obtained by phase spectrum of Fourier Transform, which converts the dim target detection as salient region extraction. The target is finally extracted by combining the two maps. Moreover, the false alarms are excluded by exploiting the correlation of targets’ position in the two maps. In this paper, we use measured images and simulated images to examine the performance of the proposed method. 9 targets in 7 different backgrounds are all detected without false alarm by the proposed method while top-hat method and local contrast method are not able to detect them all. It is validated that the proposed method has a better performance on effectiveness and robustness for detecting dim targets in different backgrounds. The weighted gray map processing can improve the contrast between targets and backgrounds compared with the top-hat method according to the SCRGs. The ROC curves based on the simulated images show that the proposed method outperforms top-hat method, facet model method, local contrast method, TDLMS method for low SCR targets detection. In summary, the novel method is robust and effective for infrared dim targets detection. Conflict of interest The authors declare that they have no conflict of interests concerning the content of this study. References

Fig. 13. The ROC curves under different SCR. The SCR of (a), (b), (c) is 2, 2.5, 3 respectively.

4. Conclusions Because of lack of texture information, less pixels, low contrast, fuzzy edge, and diverse in background texture, the dim target detection, especially the low SCR target detection in complex backgrounds, is still rather difficult and challenging. In this paper, a robust infrared dim target detection algorithm based on template filtering and saliency extraction is proposed. The weighted gray

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