Applied Soft Computing Journal 76 (2019) 671–681
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Applied Soft Computing Journal journal homepage: www.elsevier.com/locate/asoc
Speckle noise removal in SAR images using Multi-Objective PSO (MOPSO) algorithm ∗
R. Sivaranjani a , , S. Mohamed Mansoor Roomi b , M. Senthilarasi b a b
Department of ECE, Sethu Institute of Technology, Virudhunagar, Tamilnadu, India Department of ECE, Thiagarajar College of Engineering, Madurai, Tamilnadu, India
highlights • • • • •
A MOPSO framework for despeckling of SAR image is proposed. Threshold values are optimized for transform domain despeckling. Both reference metric and no reference metric is considered for experimentation. The experimental evaluation is carried out on simulated speckle affected image to validate the power of algorithm with reference metric. It is proved that the proposed method is robust and efficient against MODE.
article
info
Article history: Received 12 June 2018 Received in revised form 4 November 2018 Accepted 22 December 2018 Available online 4 January 2019 Keywords: Dual tree complex wavelet transforms SAR image Speckle noise MOPSO No reference metric
a b s t r a c t SAR images are inherently affected by speckle noise, and although attempts made earlier to remove speckle succeeded, there is still the challenge of preserving the edges of images. This is due to the smoothing effect of most of the earlier algorithms that work on thresholding coefficients in the transform domain. There exists a trade-off between denoising and the ability to preserve edges in selecting a suitable threshold. Estimation of an optimal threshold is a major concern and is compounded by the requirement for concurrent smoothing of noise and preservation of structural/edge information in an image. Considering the search for an optimal threshold as exhaustive and the requirements as contradictory, we model this as a Multi-Objective Particle Swarm Optimization (MOPSO) task and propose a MOPSO framework for despeckling an SAR image using a Dual-Tree Complex Wavelet Transform (DTCWT) in the frequency domain. Two counteractive reference metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index Metric (MSSIM), and non-reference metrics such as the alpha-beta (αβ ) and Despeckling Evaluation Index (DEI) have been used as the objective functions of MOPSO. An optimal threshold derived from this multi-objective optimization is chosen for despeckling the SAR images. The proposed solution has been found to outperform state-of-the-art filters such as Lee, Kaun, Frost and SARBM3D filters. Also, the proposed MOPSO framework superior than the competing optimization technique Multi-Objective Evolutionary Algorithm (MOEA) based on Differential Evolution (DE) framework for despeckling. © 2018 Elsevier B.V. All rights reserved.
1. Introduction Synthetic Aperture Radar (SAR) is an active remote sensing system that has the ability to acquire images in the dark and in all weather conditions, thus possessing a larger dynamic range than optical images. SAR images are inherently affected by a signaldependent noise known as speckle [1–3], which is due to the coherent nature of the radar imaging process. Automatic Target Recognition (ATR) in SAR images plays an essential role in both ∗ Corresponding author. E-mail addresses:
[email protected] (R. Sivaranjani),
[email protected] (S.M.M. Roomi),
[email protected] (M. Senthilarasi). https://doi.org/10.1016/j.asoc.2018.12.030 1568-4946/© 2018 Elsevier B.V. All rights reserved.
national defence and civil applications. For good performance of SAR ATR, despeckling is a key factor, aiming to capture the characteristics of targets such as edges, texture and shape by smoothing out the grainy speckle noise pattern. The various military targets in the MSTAR dataset are influenced by speckle noise, as shown in Fig. 1. Speckle is a granular disturbance, usually modelled as multiplicative noise. The presence of such radar speckle noise [4] visually degrades the appearance of images and severely diminishes the performance of automatic scene segmentation. Therefore, speckle reduction is a critical preprocessing step in segmentation, target detection and classification in SAR image processing. There are many filtering approaches in the literature, and these can be generally divided into two categories, namely, filtering in the spatial domain and filtering in the transform domain.
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Fig. 1. Speckle affected MSTAR 1 dataset images.
1.1. Spatial domain filters A spatial filter consists of a moving window sliding over the image pixel by pixel and substituting the value of the central pixel with a mathematically derived value from the local region. This results in a smoother appearance and reduces the speckle noise. Many denoising techniques have been developed to improve SAR image quality and each has advantages and limitations. Numerous despeckling filters have been proposed [5–11]. Lee [12] developed a linear local statistic filter that minimizes the mean-square error for reducing speckle in images. Lee also proposed a sigma filter that preserves strong edges and blurs weak edges. From Lee’s studies, it can be noted that two individual approaches were used to combine the effects of despeckling and edge preservation. Tomasi et al. [13] introduced a non-linear filter denoted the bilateral filter, that combines spatial and range filters to smooth the images while preserving edge information. In this method, large-scale structures only are preserved, whereas small structures are removed since they are considered to be noise. Buades et al. [14] developed a structure-preserving denoising technique based on minimizing the Bayesian risk. It gives excellent performance but has significant complexity. Yu et al. [7] proposed a modified anisotropic diffusion filter called Speckle-Reducing Anisotropic Diffusion (SRAD), which removes the speckle noise efficiently but blurs the low-contrast edges. 1.2. Transform domain filters The effectiveness of spatial filters mainly depends on the size and orientation of the local window. Initially, transform-domain filters evolved from Fourier transforms [15], which are localized only with respect to the frequency aspect. Later they were extended to an alternative mathematical tool, namely, wavelet transforms, providing an analysis of the signal localization for both time and frequency. Wavelet methods provide an appropriate basis for isolating noise pixels from image pixels. Filtering in the wavelethomomorphic domain has been extensively used in preference to conventional spatial filters. From the literature, it is found that wavelets provide better reduction of speckle noise than spatialdomain filters. The multiscale wavelet has been widely applied in the field of SAR image processing. The despeckling techniques in the wavelet domain guarantee a superior ability to preserve signal resolution but suffer from ringing effects near the edges of the images. The Discrete Wavelet Transform (DWT) [16] is very efficient from the computational point of view but it suffers from translation variance. In order to overcome this, the UnDecimated Wavelet Transform (UDWT) [17–19] has been proposed, which applies the wavelet transform but omits downsampling in the forward wavelet transform and upsampling in the reverse transform. However, the computational complexity is greater. The DualTree Complex Wavelet Transform (DTCWT) [20–22] has alleviated
the drawbacks of DWT and UDWT. DTCWT has the advantage of enhanced directional selectivity, near shift invariance and limited redundancy. Generally, noise removal is accomplished through thresholding of transformed coefficients. Donoho [23] introduced wavelet thresholding methods for noise removal in which wavelet coefficients are thresholded in order to remove their noisy part. The well-known wavelet shrinkage universal threshold technique [24, 25] has the disadvantage of over-smoothing during despeckling of SAR images. Later, it was enhanced through Stein’s Unbiased Risk Estimator (SURE). BayesShrink shows superior performance to the SURE shrink threshold method in terms of MSE. The reconstruction using BayesShrink is smoother and more visually appealing than that obtained using the SURE method. The key step in threshold-based transform-domain despeckling involves setting up the threshold and determining suitable coefficients based on this. Therefore, two basic problems are to be addressed during this step. They are: threshold determination and selection of the optimal threshold function. Optimization is a way to meet this requirement that mainly originates from evolutionary computing, where Swarm Intelligence (SI) can be characterized as the searching behaviour of savvy swarms to obtain the optimal threshold in frequency-domain filtering. Various evolutionary algorithms, such as particle swarm optimization, genetic algorithms and ant colony optimization, are employed in the literature for the selection of optimal values for despeckling. Li et al. [26] proposed an approach that employs Particle Swarm Optimization (PSO) on the curvelet transform, which is used to successfully reduce speckle noise. Most studies were carried out by considering a single objective function for removing speckle noise. However, a single objective function cannot satisfy the criteria for removing speckle as well as retaining edge information. Hence, a multi-objective approach for despeckling is necessary. Multi-Objective Optimization (MOO) [27] is a method of optimizing at least two conflicting objectives simultaneously, subject to certain constraints. Vast despeckling algorithms either preserve details or suppress noise. Here, a multi-objective algorithm is used to handle the trade-off between noise removal and edge preservation. The selection of the threshold for the transform-based denoising problem can be determined using the Multi-Objective Particle Swarm Optimization (MOPSO) technique [28,29]. The objective functions that are prevalent for validating the noise removal capability are the Signal-to-Noise Ratio (SNR), Peak Signalto-Noise Ratio (PSNR), Mean-Square Error (MSE), Energy Signalto-Noise Ratio (ESNR), Equivalent Number of Looks (ENL) and αβ . Those for preserving edges are the Figure of Merit (FoM), Mean Structural Similarity Index Metric (MSSIM), Edge Correlation (EC), coefficient of variation and Despeckling Evaluation Index (DEI). A MOPSO-based filtering strategy can be evaluated with referencebased assessment metrics such as PSNR and MSE. However, in
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real scenarios for SAR image acquisition, it is impossible to obtain a speckle-free SAR image. How then can we evaluate the despeckling algorithm with a reference image? To resolve this issue, a non-reference performance metric is needed, to evaluate the MOPSO-based SAR image despeckling algorithm. In this study, Multi-Objective Particle Swarm Optimization (MOPSO) is used to determine the best threshold values for optimal despeckling. The contributions of the paper are as follows.
• Selection of a suitable cost/objective function for optimiza• • •
• •
tion to satisfy multi-objective criteria for despeckling and edge preservation in SAR images. Design of a MOPSO model with reference and non-reference objective functions. Implementation of a MOPSO-tuned threshold for the DTCWT speckle reduction model. Analysis of the proposed MOPSO-tuned DTCWT method on simulated speckle-affected images with a reference-based objective function. Analysis of the proposed method on SAR images with a nonreference-based objective function. Evaluation of the improvement of quality resulting from the proposed non-reference MOPSO despeckling model for target detection.
The rest of this paper is organized as follows. Section 2 explains the proposed methodology. The experimental results and analysis are provided in Section 3, and a brief conclusion is given in Section 4. 2. Proposed methodology A multi-objective particle swarm optimization model is developed to remove the speckle noise from SAR images by considering two contradictory functions for preserving edges and removing noise. An input speckle-affected SAR image is subjected to log transformation and a dual-tree complex wavelet transform. An exponential function is chosen as the threshold function, to decide whether the coefficients contribute to the noise or the data. This threshold function is modelled using two parameters, such as a shape factor (γ ) and its threshold value (T). These parameters for despeckling transformed coefficients are obtained via the MOPSO model. A pair of objective functions is chosen to regulate the despeckling algorithm with regard to its edge preservation and noise removal capability. Two pairs of objective functions are defined here: one for the reference-based and the other for the non-reference-based approach. In the proposed model, PSNR and MSSIM are chosen as the objective functions for the referencebased approach and the αβ metric and DEI are used for the nonreference-based approach, to select the optimal threshold value via MOPSO. The optimal threshold value obtained from MOPSO is utilized to decide whether the coefficients are prone to noise or not. Based on this decision, the coefficient chosen after thresholding is fed to the inverse DTCWT and undergoes antilog transformation to obtain the resultant despeckled SAR image. The proposed MOPSO model for despeckling SAR images is shown in Fig. 2 and the necessary explanation is given below. 2.1. Speckle noise model in SAR image The information in SAR images is important for automatic target detection and target classification, but speckle noise causes degradation of these images. Hence, speckle reduction is a necessary procedure. The speckle noise can typically be modelled as multiplicative independent and identically distributed (i.i.d.) Gaussian noise with zero mean and unit variance. 1 Available online: https://www.sdms.afrl.af.mil/datasets/mstar/.
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Let yi,j be the degraded noisy pixel, then yi,j = xi,j ∗ ni,j
(1)
where xi,j and ni,j are the speckle-free image pixel and multiplicative noise respectively at location (i, j). The goal is to recover xi,j from yi,j . The subscripts i, j represent the spatial position of the pixel in the image. Taking a logarithmic function on both sides of the equation results in the following equation. ln(yi,j ) = ln(xi,j ) + ln(ni,j )
(2)
In SAR image processing and analysis, the logarithmic transform is often used to convert the multiplicative speckle model to an additive noise model. To compensate for the logarithmic effect, a final exponential operation is performed to obtain an estimate of the speckle-free SAR image. 2.2. Dual Tree Complex Wavelet Transform filtering In non-linear despeckling techniques, wavelet-based image denoising methods have attracted some attention, due to their multiresolution characteristics. In spite of its computational efficiency and sparse representation, the wavelet transform suffers from four fundamental intertwined shortcomings: oscillations, shift variance, aliasing and lack of directionality. To overcome the shortcomings of the wavelet transform, the Non-Subsampled Contourlet Transform (NSCT) was proposed by Da Cunha et al. [30]. However, the NSCT algorithm is time-consuming and highly complex. Compared to the discrete wavelet transform, the Dual-Tree Complex Wavelet Transform (DTCWT) has the advantage of enhanced directional selectivity, near shift invariance, limited redundancy, near-ideal reconstruction and less computational complexity than UDWT and NSCT. In the wavelet transform, high-pass filtered original images produce three detailed sub-bands and low-pass filtered images produce an approximate sub-band. This approximate sub-band is further downsampled into detailed and approximate sub-bands at each level. However, the pair of trees is applied to produce six high-pass sub-bands as well as two low-pass sub-bands at each level of DTCWT decomposition. Each filtering operation is followed by downsampling with a sampling factor of two. Six directional wavelets of DTCWT are obtained by taking the sums and differences of high-pass sub-bands which are in the same passband. The DTCWT of an image results in an approximate sub-band and six directionally selective sub-bands for both the real and imaginary parts at each level, which are strongly oriented at angles of ±15◦ , ±45◦ and ±75◦ . In general, the dual-tree wavelet coefficients for level L are represented as {WR,l,d , WI ,l,d }, where l = 1, 2, . . . , L and d = 1, 2, 3, 4, 5, 6. This represents the lth level of the dth direction real and imaginary wavelet coefficients. Fig. 3 shows the different directions of the DT-CWT coefficients for level 3 decomposition of an image. The row and column implementation of separable 2D wavelets is mathematically described as
ψ (x, y) = ψ (x) ψ (y)
(3)
If ψ (x) and ψ (y) are complex wavelets along a row and column, then this can be mathematically represented as
ψ (x) = ψh (x) + ψg (x)
(4)
ψ (y) = ψh (y) + ψg (y)
(5)
Substituting Eqs. (5) and (6) into (4) results in
( )( ) ψ (x, y) = ψh (x) + jψg (x) ψh (y) + jψg (y)
(6)
where ψg and ψh are low-pass and high-pass filter components.
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Fig. 2. Proposed Experimental setup.
Fig. 3. Level-3 2D-DTCWT complex sub bands.
2.3. Selection of threshold function The noisy detailed coefficients of DTCWT can be determined using a thresholding method. Thresholding of the wavelet coefficients is generally applied only to the detailed coefficients and not to the approximate coefficients, since the latter represent ‘lowfrequency’ terms that usually contain significant components of the signal and are less affected by noise. The proficiency of the threshold-based method depends on two important factors. One is the threshold and the other is the selection of the threshold function. Two predominant thresholding schemes are hard threshold and soft threshold schemes [31]. The hard threshold scheme removes coefficients below a threshold value T, and the value of T is determined by the noise variance. This is sometimes referred to as the ‘keep or kill’ method.
w ˆ k,l =
{
⏐ ⏐ wk,l , ⏐⏐wk,l ⏐⏐ ≥ T , ⏐wk,l ⏐ < T . 0,
(7)
The soft threshold scheme shrinks the wavelet coefficients above and below the threshold, as expressed in Eq. (9). The soft threshold provides smoother results compared to the hard threshold, whereas the latter provides better edge preservation than the former
w ˆ k,l =
{
sgn wk,l 0,
(
) (⏐ ⏐ ) ⏐ ⏐ ⏐wk,l ⏐ − T , ⏐wk,l ⏐ ≥ T , ⏐ ⏐ ⏐wk,l ⏐ < T .
threshold functions. In this way, exponential threshold-based denoising provides better performance. The exponential threshold function is mathematically defined as
{ ψk,l =
sgn wk,l 0
(
) {⏐ ⏐ ⏐wk,l ⏐ −
} ⏐ ⏐ ⏐wk,l ⏐ ≥ T , ⏐ ⏐ [γ (|wk,l |−T )/T ] ⏐wk,l ⏐ < T . T
exp3
(9)
where wk,l can be flexibly adjusted by changing the shape parameter γ . This is an ordinary number which can be balanced liberally, with a distinctive value for different signals. The factors in Eq. (10) are also very important, and a change in the value of γ can influence the noise directly. When ‘γ ’ tends to zero, the function tends to be a soft threshold and when ‘γ ’ tends to infinity the function manifests as a hard threshold. In this exponential thresholding method, the adjustable factors are ‘γ ’ and T. These parameters are chosen through optimization. To simultaneously meet the requirements for smoothing and preserving details, two different objective functions are used for optimization. MOPSO optimization helps to find the optimal values of the modelling parameters (‘γ ’ and T) of the exponential threshold function. 2.4. MOPSO framework for SAR image despeckling
(8)
A new exponential threshold overcomes the inadequacies of discontinuous and constant deviation in taking decisions and incorporates the benefits of the soft, hard, semi-soft and Garrote [32]
The transform-domain despeckling approach mainly depends on the threshold to suppress multiplicative noise, where the threshold is controlled via the tuning parameters (γ and T). An exhaustive search mechanism is required to select an optimal
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Table 1 MOPSO parameters. Parameters
Value
Number of Decision Variables(nd ) Number of Cost function(nc ) Cost Function Maximum Number of Iterations(N) Population Size(p) Repository Size(r) Inertia Weight(w) Inertia Weight Damping Rate(wdamp) Personal Learning Coefficient(C1) Global Learning Coefficient(C2) Leader Selection Pressure(λ) Deletion Selection Pressure(ψ ) Mutation Rate(µ)
2 2 PSNR& MSSIM (or) αβ &DEI 10-100 10-100 10 0.5 0.9 1 1 2 2 0.2
threshold for a better solution from every possible value, by varying these tuning parameters in each trial. These trials lead to an exhaustive search, which is computationally expensive. In order to reduce the exploration capability of the search domain with respect to exhaustive searching, an optimization technique can be used. Among the population-based methods, PSO requires less computation time and less memory, because of its inherent simplicity. Generally, PSO handles a single objective with a fast convergence speed, but here it is required to handle two conflicting objectives, i.e., speckle noise removal and edge preservation. The multi-objective optimization technique finds the best possible solutions that satisfy all objectives and constraints. MOPSO is an evolutionary algorithm [33–35] that is used to find a solution from the solution space by considering more than one objective function. Similarly, the Multi-Objective Evolutionary Algorithm (MOEA) based on Differential Evolution (DE) is another evolutionary algorithm [36] which decomposes the multi-objective optimization problem into several single-objective subproblems. In MODE, the best solution is found by comparing all the possible solutions for each subproblem/objective, whereas in MOPSO the objectives are handled simultaneously to provide optimal solutions for noise reduction and for the preservation of edges. MOPSO was the only algorithm in the study that was able to cover the full Pareto front of all the functions used. Hence, the SAR image despeckling problem is modelled using the Multi-Objective Particle Swarm Optimization (MOPSO) technique. The main advantages of MOPSO are its ability to handle two objectives simultaneously, short convergence times and low computational overheads compared to MODE. MOPSO uses a mutation operator that acts on both the individuals and on the ranges of each decision variable to be explored. This unique method not only considers the diversity in the objective space but also uses a full search in the decision space, via region-based selection rather than individual selection. The algorithm for SAR image quality improvement by despeckling with MOPSO tuning is given below. The MOPSO system is initialized with a population consisting of particles in the search space, and the particle positions and velocities are computed and updated subject to the cost/gain function. The MOPSO parameters used for the experiments are listed in Table 1. The MOPSO system is initialized with a population consisting of particles in the search space. At first, the particle position is chosen arbitrarily. In the initialization step, some random particles are initialized in the search space, where each one has a number of tweak parameters (chosen by the user). That is, each particle xi is constructed such that xi = (T11, T12,...... Tij ), where Tij is the jth tweak factor of the ith particle. In this proposed problem, two tweak factors α and T are chosen. The dual-tree complex wavelettransformed coefficients in the approximate band are retained as such and only HH subbing is used for thresholding. This is because the noise is mainly concentrated in the high-frequency subband with the smallest scale. By utilizing the random threshold α
Fig. 4. MOPSO enabled Despeckling flow.
and T initially provided by MOPSO, the complex coefficients are thresholded, and then the inverse transform is applied. Finally, the cost functions for denoising and edge-preserving metrics are computed simultaneously for all particles. Personal best and global best values are updated for each particle in the population. This procedure is iteratively repeated for various threshold values until the optimal solution is found by moving towards the global best in each iteration. The proposed MOPSO-enabled despeckling flow is shown in Fig. 4. In the proposed method, the objective functions, (e.g., denoising and edge-preserving metrics) for MOPSO tuning are considered as two cases, based on the usage of the reference image in their functions. In order to verify the efficacy of the proposed MOPSObased despeckling algorithm on simulated speckle images, the objective functions are produced using full reference metrics such as PSNR and MSSIM, as specified in case 1. However, in a real scenario, the availability of reference SAR images is impossible. Hence, non-reference metric-based MOPSO tuning is implemented for SAR images, as discussed in case 2. Case 1 - Full Reference (FR) Measure The full reference objective functions chosen are the Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index Metric (MSSSIM). Both of these objectives are maximized to obtain a smooth response in homogeneous regions and sharpening in heterogeneous regions of an image. A higher value of PSNR reflects
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Fig. 5. MSSIM values for various filters compared with proposed.
Fig. 6. PSNR values for various filters compared with proposed.
better quality in the despeckled image PSNR(ID , IR ) = 10 log10
MSE (ID , IR ) =
[
( )
IR 2Peak
(10)
MSE
M N 1 ∑∑
MN
]
(IR (i, j) − ID i, j)2
(11)
i=1 j=1
The mean structural similarity index is another full reference metric that emphasizes the edge-preserving ability. The Structural Similarity Index Metric (SSIM) is used to measure the similarity between two images. The range for the MSSIM index is between zero and one, where one represents good edge-preserving ability. The computation of MSSIM is shown below M −1 1 ∑
MSSIM (ID , IR ) =
M
( SSIM (Id , IR ) = (
SSIM(ID , IR )
(12)
p=0
2µId µIR + c1
µ2ID + µ2IR + c1
)(
2.σID IR + c2
)(
)
σI2D + σI2R + c2
)
(13)
where M is the number of local windows in the image, ID & IR and are the despeckled and reference images and c1 & c2 are constants. The terms µ and σ correspond to the mean and standard deviation. In this case, the objective functions J (I), (e.g., PSNR and MSSIM) are maximized to find the optimal solution, as expressed in Eq. (15). ∧
I = arg(max(J(I)))
(14)
Evaluation Index (DEI), are suitable for SAR image despeckling via MOPSO tuning. In this case, regarding the two conflicting issues, the noise-reducing capability of a filter is estimated using the estimator [37] and the edge-preserving capability is calculated via the DEI [38] of the Edge Around Region (EAR). The αβ estimator is defined as
⏐ ⏐} { (15) αβ = α. |δENL | + (1 − α) . ⏐δµ ⏐ + β ( ) )( ( ) ) ∑K ( (Ih )ratio − Ih ratio i=1 (Ih )noisy − Ih noisy β= ( ) 2∑ ( ) ( ( ) )2 (16) ∑K K I − I I − Ih ratio ( ) ( ) h h h noisy ratio i=1 i=1 noisy where Ih is the high-pass filtered image, I h is the average of the filtered image, δENL is the Equivalent Number of Looks (ENL), which differs between the noisy image and the ratio image and δµ is the mean of the ratio image. The αβ estimator has typical values between zero and one. The Despeckling Evaluation Index (DEI) is used to assess the edge-preserving ability of filtering techniques. This is the ratio of the standard deviation over a smaller neighbourhood window to that over a larger neighbourhood window. The DEI value should be less than unity. If the standard deviation of values in the window is large, then it consists of more edges. Therefore, a smaller DEI results in better edge-preserving ability.
( ( )) ∑ min std wpm,q ( ) DEI = M ∗N std wxn,y x, y 1
(17)
Case 2 - No reference (NR) Measure
Here, the objective function should have minimal values for better edge maintenance and noise removal. Hence, the minimization of the objective function is required, as in Eq. (18).
Non-reference metrics, such as the alpha-beta (αβ ) metric based on the ratio of the edge estimator and the Despeckling
Iˆ = arg(min(J(I)))
I
I
(18)
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Thus, the quality of the SAR image is improved by despeckling using the optimal values in the exponential threshold function for HH sub-band coefficients 3. Experimental results and analysis The proposed SAR image despeckling technique using MOPSO was realized using MATLAB 2015 simulation software. The performance assessment for SAR image despeckling is not straightforward, because the original unspeckled SAR image is unknown. To validate the effectiveness of the proposed algorithm, the test is performed first on the standard images with simulated speckle and later validated on SAR images. MOPSO despeckling with the reference metric as the objective function is tested on speckled classical images, such as Lena, Barbara, house and peppers. In addition, MOPSO with non-reference metric despeckling is evaluated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database, which consists of SAR images of a military tank, bulldozer, etc. The input image is subjected to log transformation and DTCWT. While converting the input image to the DTCWT transform domain, an Antonioni filter is used for the first-level decomposition and a 14-tap Q-shift filter is used for the following two levels of the DTCWT. The quality of the MOPSO-enabled denoising can be evaluated using full reference measures, such as PSNR and MSSIM, on speckled standard classical images. In this case, both the original speckle-free images and those including speckle are known, and the despeckling results can be visually compared with the original images as well as evaluated using objective reference metrics. The exponential threshold performance is compared with the conventional threshold used in the literature. The test was carried out for a different population set and various iterations. As shown in Table 2, the exponential threshold outperforms other thresholding schemes in terms of PSNR and MSSIM. From Figs. 5 and 6, we can see that the proposed method can achieve a larger MSSIM value and a larger PSNR than traditional methods. The MOPSO tuning to obtain an optimized value of the threshold by maximizing PSNR and MSSIM is shown in Fig. 7. The objective functions, i.e., PSNR and MSSIM, are maximized to obtain the optimal value in the simulated speckled images. The proposed method is compared with various state-of-the-art filters in terms of PSNR and MSSIM. Fig. 8(a) shows the speckle noise simulated in the classical Lena image. The state-of-the-art filters such as the Lee filter, Frost filter, anisotropic diffusion filter, sigma filter and Lee diffusion and Frost diffusion filters, are applied to the corrupted image, and their respective results are shown in Fig. 8(b)–(g). The results of the proposed MOPSO-based despeckling system are given in Fig. 8(h), which reveals that the proposed filtering is very effective for homogeneous regions as well as for heterogeneous regions. Experiments were carried out for different levels of decomposition and different levels of noise variance, and it was observed that level 3 decomposition provided better results. The proposed despeckling algorithm is implemented on an Intel Core i3-6006U CPU with RAM of 8 GB in the MATLAB environment. The experimentation has been done by varying noise variance from 0.005 to 0.05 to test the robustness of proposed algorithm. The comparative result of MOPSO based despeckling against the competing optimization MODE in terms of PSNR and MSSIM are tabulated in Table 3 on simulated speckled Lena images. MOPSO quantitatively provides maximum PSNR of 34 dB and MSSIM of 0.93 for simulated speckled Lena image with 0.005 noise variance level. This comparison has evidenced that the proposed MOPSO techniques has outperformed MODE based despeckling algorithm with the increase of around 3 to 4 dB PSNR at different noise variance. It justifies the robustness of the proposed approach. From the experimentation, it is inferred that irrespective of noise variance, MOPSO-based despeckling provides excellent noise removal
Fig. 7. MOPSO Tuning by maximizing PSNR and MSSIM.
as well as edge preservation, even with the minimum number of iterations. Since the computational complexity analysis is an important aspect, the computational CPU time is calculated for both MOPSO and MODE based despeckling. The computational time consumed for both these methods are calculated by performing the despeckling on simulated speckled Lena image at various noise level as shown in Fig. 9. The average computational time taken for the proposed MOPSO technique was around 13 s; around half the time required by the competing MODE technique. Moreover, the performance of MOPSO is better than MODE when assessed in terms of PSNR and MSSIM. For non-real-time applications like satellite SAR imagery, the proposed technique may be used directly, whereas for applications such as despeckling for target detection, though the computational time consumed by the proposed MOPSO is marginally higher, it can be further reduced via hardware implementation. Hardware implementation of PSO using FPGA increases the speed of computation to a great extent, to up to 28,935 times faster [40] than the simulation counterpart. The proposed image despeckling framework is applied to the Moving and Stationary Target Acquisition and Recognition (MSTAR) [41] database images. The input size of the image is taken as 128 × 128. The non-reference objective metrics such as DEI in Edge Around Region (EAR) and the alpha-beta metric, are minimized for optimal threshold parameters. If the despeckling is perfect, then the further processing of the SAR image is quite easy. One of the biggest tasks in SAR image processing is target detection, which is useful for military applications. The despeckled images are further subjected to an adaptive threshold based on entropy [42], to extract geometric features to detect targets. Fig. 10(a) is the original SAR image from the MSTAR data set, which is subject to various filters such as the Lee filter, Frost filter, anisotropic diffusion filter, sigma filter and Lee diffusion and Frost diffusion filters. The results of target detection on the filtered outputs are shown in Fig. 10(b)–(g). The target detection after the proposed MOPSO filtering attained proper detection compared with others, as shown in Fig. 10(h). In contrast to other filter outputs, the proposed technique is significantly more effective in smoothing speckle over uniform regions and enhancing edges to detect the target position precisely with a low false alarm rate. Figs. 11 and 12 show the DEI and alpha-beta metrics for different filtering schemes. The proposed filtering strategy gives the minimum value, which ensures better noise suppression and edgepreserving ability. The edge-preserving metrics MSSIM and DEI are used here to prove the edge-preserving ability of the proposed
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Table 2 Comparative analysis of various thresholding schemes. Scheme
Iteration
Parameter
PSNR (dB)
MSSIM (Unitless)
Hard (T)
10 100
10 10
0.2785 0.2813
28.7617 28.7706
0.8309 0.8323
Soft (T)
100 100
10 10
0.1511 0.1542
28.0410 28.0098
0.8097 0.8117
Semisoft(T)
100 100
10 100
0.1987 0.2006
29.0124 29.0059
0.8314 0.8325
Garrote (T)
10 10
10 100
0.2073 0.2164
28.5482 28.4273
0.8328 0.8336
10 100
10 10
(0.2362, 1.4197) (0.2152, 0.9817)
28.9407 30.7315
0.8348 0.8757
Exponential (α ,T)
Population
Fig. 8. (a) Noisy Lena image, (b)–(f) Despeckled images using various filters (b) Lee [12], (c) frost [5], (d) Anisotropic diffusion [7], (e) sigma [39], (f) Lee diffusion [7], (g) Frost Diffusion [7] and (h)Proposed filtering.
Table 3 Comparison of proposed MOPSO algorithm with MODE in different noise variance level. Noise variance
Fig. 9. Comparison of Computational time-MOPSO Vs MODE.
algorithm compared with other state-of-the-art filters. The higher
0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05
PSNR(dB)
MSSIM(Unitless)
MOPSO
MODE
MOPSO
MODE
34.09 33.52 30.90 30.11 29.20 28.86 28.55 27.88 27.25 26.68
30.78 30.15 28.23 26.66 25.12 26.47 26.48 25.98 25.65 24.83
0.93 0.90 0.87 0.86 0.83 0.83 0.82 0.80 0.78 0.75
0.88 0.87 0.81 0.79 0.75 0.74 0.72 0.71 0.70 0.70
4. Conclusion
MSSIM and lower DEI of the proposed approach show its superior ability for preserving edges compared to other filters. The MOPSOtuned despeckling has improved the performance of target detection, as shown in Fig. 13. MSTAR targets such as canon, bulldozer, truck and armoured car are detected.
Speckle noise heavily corrupts the appearance of images and seriously reduces the potential for target arrangement and identification. In this work, a new SAR image despeckling algorithm in the transform domain with MOPSO modelling is proposed. A dual-tree complex wavelet transform is applied on speckle-affected images,
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Fig. 10. (a) Original SAR, (b)–(h) Target position after applying adaptive threshold in filtered image and (b) Lee filter [12] (c) Frost filter [39] (d) Anisotropic diffusion [7] (e) Sigma filter [39] (f) Lee diffusion [7] (g) Frost diffusion [7] (h) Proposed method.
Fig. 11. DEI values of edge around region (EAR) for various filters compared with proposed method for SAR Images.
Fig. 12. Alpha-beta values of edge around region (EAR) for various filters compared with proposed method.
with a threshold given by the exponential function. The two control
MOPSO to maximize PSNR and MSSIM or to minimize the αβ esti-
parameters of the threshold function are tuned and selected by
mator and DEI. The proposed method has strong speckle-reduction
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Fig. 13. Top row: Test SAR image from MSTAR database, bottom row : Detected target after despeckling using proposed method.
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