A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation

Abstract

In this paper, we propose a boosting synthetic aperture radar (SAR) image despeckling method based on non-local weighted group low-rank representation (WGLRR). The spatial structure information of SAR images leads to the similarity of the patches. Furthermore, the data matrix grouped by the similar patches within the noise-free SAR image is often low-rank. Based on this, we use low-rank representation (LRR) to recover the noise-free group data matrix. To maintain the fidelity of the recovered image, we integrate the corrupted probability of each pixel into the group LRR model as a weight to constrain the fidelity of recovered noise-free patches. Each single patch might belong to several groups, so different estimations of each patch are aggregated with a weighted averaging procedure. The residual image contains signal leftovers due to the imperfect denoising, so we strengthen the signal by leveraging on the availability of the denoised image to suppress noise further. Experimental results on simulated and actual SAR images show the superior performance of the proposed method in terms of objective indicators and of perceived image quality.

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

“…In recent years, machine learning-based methods are proposed and show promising results in the field of SAR image despeckling [19]- [25]. These methods mainly include: low-rank representation [19], [20], sparse representation [21], [22], and deep convolutional neural network (CNN) [23]- [25]. In [19], similar patches are grouped and low-rank constraint is used to remove the speckle noise.…”

A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

“…Generally, a large STD corresponds to higher speckle noise. The standard methods used for comparison are GF, SAR-BM3D, Bilateral filter, Fast Bilateral filter, WLS, DPAD, DPAD, and the proposed method of Fang et al [42]. Table 9 presents the parameters of different filters.…”

Lightweight Image Restoration Network for Strong Noise Removal in Nuclear Radiation Scenes

“…Therefore, in addition to the above existing denoising methods for radiation scenes, general denoising algorithms focused on additive noise removal may also have good denoising performances in radiation scene images. The existing general image denoising methods fall into two categories: image prior-based methods [ 11 , 12 , 13 , 14 , 15 ] and discriminative learning methods [ 16 , 17 , 18 , 19 , 20 , 21 ].…”

“…In a different way, Dong et al used the sparsity prior to natural images and proposed the Nonlocally Centralized Sparse Representation (NCSR) algorithm [ 12 ] for noise removal. Gu et al proposed a denoising algorithm named Weighted Nuclear Norm Minimization (WNNM) [ 15 ], combining the Non-local Means method (NLM) [ 13 ] with Low-rank Representation (LRR) [ 14 ]. Though theoretically clear, these SOTA image prior-based methods are time consuming due to their multiple iterations.…”