Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Zhang, Junchao; Chen, Jianlai; Yu, Hanwen; Yang, Dongkai; Xu, Xiaoqing; Xing, Mengdao

doi:10.1109/jstars.2021.3097119

Cited by 12 publications

(6 citation statements)

References 37 publications

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“…Then, SAR image filtering based on dictionary learning and sparse representation [22] adds a preprocessing step of logarithmic conversion of the image before K-SVD image despeckling. The SAR image-denoising model based on weighted sparse representation [23,24] performs similar block matching on the logarithmic image of the original SAR image, and then performs K-SVD image denoising. This SAR image filtering based on dictionary learning and sparse representation applies the sparse representation filtering idea to SAR image despeckling, showing good results in speckle noise suppression.…”

Section: Sparse Representationmentioning

confidence: 99%

Non-Local SAR Image Despeckling Based on Sparse Representation

Yang,

Yu,

et al. 2023

Remote Sensing

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Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms.

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Section: Sparse Representationmentioning

confidence: 99%

Non-Local SAR Image Despeckling Based on Sparse Representation

Yang,

Yu,

et al. 2023

Remote Sensing

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“…In Ref. 41, a weighted sparse representation-based approach for despeckling SAR images is described. First, multiplicative noise is transformed into additive noise via the homomorphic transformation.…”

Section: Journal Of Electronic Imagingmentioning

confidence: 99%

Review on nontraditional perspectives of synthetic aperture radar image despeckling

2022

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Synthetic aperture radar (SAR) image despeckling is a preprocessing method. SAR images are, by default, noisy in nature. The kind of noise found in SAR images is called speckle noise. The effect of this noise on SAR images is highly adverse. It degrades the quality of the SAR image, resulting in the loss of vital information. Since SAR images are inherently speckled in nature, it costs a lot of information loss. The removal of such noise from the SAR image is mandatory and is the first step. The elimination of speckle noise from SAR images is called SAR image despeckling. There are various traditional and nontraditional methods of SAR image despeckling based on Bayesian and non-Bayesian techniques. The SAR image despeckling methods based on Bayesian techniques are further subdivided into spatial and transform domains. This paper presents a comparative review of nontraditional perspectives on SAR image despeckling. The comparison is made based on methodology, objectives, merits, and demerits. Its focus is to do analysis of all the latest research done in the field of SAR image despeckling using nontraditional methods.

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“…Thus, smoothing (by local weighted averaging) is an effective image regularization method that has been used for denoising [24]. However, SAR images should not be smoothed too much, because the studies on discriminating between SAR clutter textures are important [28,29]. Therefore, to better preserve image texture details, the natural space for our computational solution is BV α (Ω), i.e., the space of functions with bounded variation.…”

Section: Introductionmentioning

confidence: 99%

Multiplicative Noise Removal and Contrast Enhancement for SAR Images Based on a Total Fractional-Order Variation Model

Zhou

Guo

et al. 2023

Fractal Fract

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In this paper, we propose a total fractional-order variation model for multiplicative noise removal and contrast enhancement of real SAR images. Inspired by the high dynamic intensity range of SAR images, the full content of the SAR images is preserved by normalizing the original data in this model. Then, we propose a degradation model based on the nonlinear transformation to adjust the intensity of image pixel values. With MAP estimator, a corresponding fidelity term is introduced into the model, which is beneficial for contrast enhancement and bias correction in the denoising process. For the regularization term, a gray level indicator is used as a weighted matrix to make the model adaptive. We first apply the scalar auxiliary variable algorithm to solve the proposed model and prove the convergence of the algorithm. By virtue of the discrete Fourier transform (DFT), the model is solved by an iterative scheme in the frequency domain. Experimental results show that the proposed model can enhance the contrast of natural and SAR images while removing multiplicative noise.

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Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Cited by 12 publications

References 37 publications

Non-Local SAR Image Despeckling Based on Sparse Representation

Non-Local SAR Image Despeckling Based on Sparse Representation

Review on nontraditional perspectives of synthetic aperture radar image despeckling

Multiplicative Noise Removal and Contrast Enhancement for SAR Images Based on a Total Fractional-Order Variation Model

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