Image Denoising via Low Rank Regularization Exploiting Intra and Inter Patch Correlation

Liu, Hangfan; Xiong, Ruiqin; Liu, Dong; Ma, Siwei; Wu, Feng; Gao, Wen

doi:10.1109/tcsvt.2017.2759187

Cited by 25 publications

(12 citation statements)

References 42 publications

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“…In this section, we conduct extensive experiments to demonstrate the performance of the proposed denoising algorithm (TSLR). In addition, we compare our algorithm with some exsiting non-deep denoising algorithms, including BM3D [6], NCSR [5], SAIST [34], WNNM [12], LIIC [15], RM [16], BMLSVDTV [25], DPID [47], and deep learning-based denoising algorithms (e.g., Dn-CNN [19], FFDNet [44] and MLEFGN [45]). Three test datasets are used to evaluate the AWGN variance σ ∈ {10, 15, 25, 30, 50, 100}.…”

Section: Resultsmentioning

confidence: 99%

“…Subsequently, compared with adopting the nuclear norm in the energy function, Huang et al [16] proposed that using matrix rank as the regularization term is better than adopting the nuclear norm in the energy function for the matrix completion problem. As different statistical features of natural images can be represented by different image priors, utilizing both intra-patch and inter-patch correlations for low-rank regularization results can achieve better denoising performance than using only one correlation source [15]. These successful works show that low-rank-based algorithms can produce good denoised images with rich features by capturing the multi-scale structure of the image.…”

Section: Introductionmentioning

confidence: 92%

“…Since the development of the NSS model, extensive further research has been devoted to exploiting sparsity-based regularization [3][4][5]25,35,37,39,40] and low-rank regularization [12][13][14][15][16]23,41,42]. Sparse representation models encode a clean image into a few basic elements of an over-complete redundant dictionary, with representative studies including [4,5,25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two-Stage Image Denoising via an Enhanced Low-Rank Prior

Fan

Shi

et al. 2021

J Sci Comput

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Restoring the image contaminated with heavy noise remains a challenging task. Since the image prior is essential to restoring a high-quality image, this paper proposes a novel twostage enhanced low-rank prior model (TSLR) for efficient image denoising. Unlike denoising an image as a whole, this algorithm divides the denoising process into two stages: contour restoration and detail restoration. First, we explore the total variation (TV) regularization term to restore the image contour, obtaining the preliminary denoised image. Although TV regularization term can reduce noise, it loses the rich details of the original image. Nevertheless, detail preservation ensures good visual quality of the denoised images. Then, to overcome the above issue, the preliminary denoised image is adopted as a rough evaluation of the original image for the second stage, and the weighted sum of the L 1 -norm and L 2 -norm is utilized as the fidelity term. Furthermore, we introduce a new enhanced low-rank prior, which combines the low-rank prior of similar patches from both gray and gradient domains, to reconstruct the fine details of the image. To further improve the validity of image denoising on the basis of the low-rank prior, the weighted nuclear norm minimization method is adopted in the present study. In addition, this work adaptively selects the search window size for different regions to accurately select similar patches. Through extensive experiments, the results reveal that our scheme can retain more detailed information while eliminating noise and can surpass a variety of advanced non-deep methods regarding both the PSNR and SSIM.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Two-Stage Image Denoising via an Enhanced Low-Rank Prior

Fan

Shi

et al. 2021

J Sci Comput

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“…This component evaluate the efficiency of the adaptive strategy vs. the fixed strategy (which is also the method proposed (18) and (19) Update y by (20) Update the noise variance σ 2 n End by Xiong et al [17]). In order to make fair comparison for the denoising results of (7) and (11), which represent a fixed strategy approach and adaptive strategy approach respectively, we let both strategies follow exactly the same patch-based denoising procedure in terms of the calculation and utilization of same regularization parameters.…”

Section: A Fixed Strategy Vs Adaptive Strategymentioning

confidence: 99%

“…The representative methods include SAIST [18] and WNNM [19]. Recently, Liu et al proposed so-called low-rank regularization algorithm using an Interand Intra-patch Correlation (LIIC) scheme [20]. Additionally some methods have extended the WNNM algorithm to other applications [21]- [24].…”

Section: Introductionmentioning

confidence: 99%

A Content-Adaptive Method for Image Denoising

Chen

Ling

et al. 2019

IEEE Access

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In this paper, we firstly analyze the statistical distribution of simultaneous sparse coding errors (SSCE), which reflects the local correlation and non-local correlation characteristics of natural images. Based on the observation, we establish the optimal denoising problem which uses L 1 norm to constrain the image prior. According to the close-form solution of the proposed problem, we find that the denoising limit is only determined by the patch number, patch size and the variances of different bands in SSCE. Then we exploit the relationships between the patch complexity, patch size, patch number and the denoising limit. The study shows that a content-adaptive strategy may be useful to obtain better denoising performance. We then design a content-adaptive method for image denoising in which the groups are adaptively determined according to the structural complexities of reference patches. Experimental results show that the proposed scheme achieves competitive performance with several state-of-the-art methods in both subjective and objective aspects.

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