SAR Image Despeckling by Using Nonlocal Sparse Coding Model

Chen, Shuxuan; Gao, Lei; Li, Quanyun

doi:10.1007/s00034-017-0704-5

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…The patch-based approach takes the advantage of non-local self-similarity (NSS) prior which is evident in case of natural images. Recently, the notion of non-local patchbased paradigm laid the foundation of several outstanding algorithms [8][9][10]18,23,27,33,36,41,52] Exceptional performance of these algorithms hinges upon simultaneous exploitation of sparsity and NSS prior information in the given image data.…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Estimation for Image Denoising Using Fractional-Order Gradient-Based Similarity Measure

Shamsi

Kim

Hussain

et al. 2021

Circuits Syst Signal Process

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The aim of this paper is to introduce a novel similarity measure using fractional-order derivative for patch comparison in low-rank image denoising approach. Recently, several outstanding low-rank image denoising algorithms have been proposed. However, these methods have limitations in the sense that certain irrelevant patches can be selected during patch comparison. These undesired patches affect singular values shrinkage and aggregation phases of these approaches. Thus, the fine details and edges of denoised image may not be well preserved. To address this issue, a novel method is proposed in which gradient information is injected in patch comparison using discretized fractional-order derivatives. The advantages of proposed approach are twofold: firstly, the patch comparison becomes more reliable by combining intensity and gradient information; secondly, the fractional-order gradient provides an additional degree of freedom to quantify the gradient information for patch comparison in an efficient way. In addition, the proposed algorithm estimates noise level using geometric details encoded in the image patches. The noise estimation strategy may help in terminating the iterative low-rank approximation. Experimental results on test images reveal that the proposed method performs better than several outstanding algorithms, specifically, in the presence of severe noise levels.

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