Image denoising via bidirectional low rank representation with cluster adaptive dictionary

Li, Xiaoping; Wang, Weiwei; Feng, Xiaoyi; Liu, Xiaoyan; Luo, Liang

doi:10.1049/iet-ipr.2016.0257

Cited by 6 publications

(1 citation statement)

References 42 publications

(62 reference statements)

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“…The method obtained an overcomplete dictionary by learning, and considers that each image block could be approximated by a linear combination of dictionary atoms, and the coefficient vector had sparsity, that is, most elements in the vector were zero. In recent years, the weighted nuclear norm minimization (WNNM) [8] and the bidirectional low rank representation denoising method based on adaptive cluster dictionary [9] decompose the image into singular values in the transform domain, and perform singular value threshold shrinkage to achieve image denoising. These two methods are still essentially denoising methods based on sparse transforms.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Based on HOSVD With Iterative-Based Adaptive Hard Threshold Coefficient Shrinkage

Gao¹,

Guo²,

Zhang

et al. 2019

IEEE Access

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Natural images often have self-similarity, which can be used to remove noise. Therefore, many current denoising methods denoise by processing similar image block matrix. Aiming at the problem that these methods will destroy the two-dimensional structure of image blocks when they are expanded into one-dimensional column vectors, a new image denoising method based on high-order singular value decomposition is proposed. Several similar image blocks are stacked into three-dimensional arrays and treated as a third-order tensor; then, higher-order singular value decomposition can be performed. For the core tensor obtained by decomposition, an iterative algorithm with adaptive hard threshold coefficient shrinkage is proposed. The experimental results show that the proposed method outperforms the state-ofthe-art methods in peak-signal-to-noise ratio, structural similarity, and visual effects.INDEX TERMS Image denoising, tensor, high order singular value decomposition, adaptive hard thresholding, threshold coefficient shrinkage.

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Section: Introductionmentioning

confidence: 99%