Image Denoising Based on Non-Local Low-Rank Dictionary Learning

2016 ◽  
Author(s):  
Zhang Bo
Keyword(s):  
Image Denoising ◽  
Dictionary Learning ◽  
Low Rank ◽  
Non Local

scholarly journals Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 158
Author(s):  
Jiucheng Xu ◽  
Yihao Cheng ◽  
Yuanyuan Ma
Keyword(s):  
Image Denoising ◽  
Block Matching ◽  
Low Rank ◽  
Self Similarity ◽  
Data Set ◽  
Noisy Images ◽  
Norm Minimization ◽  
Rank Matrix ◽  
Non Local ◽  
Low Rank Matrix

Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper proposes a new image denoising algorithm based on low rank matrix restoration in order to solve this problem. The proposed algorithm introduces the non-local self-similarity error between the clear image and noisy image into the weighted Schatten p-norm minimization model using the non-local self-similarity of the image. In addition, the low rank error is constrained by using Schatten p-norm to obtain a better low rank matrix in order to improve the performance of the image denoising algorithm. The results demonstrate that, on the classic data set, when comparing with block matching 3D filtering (BM3D), weighted nuclear norm minimization (WNNM), weighted Schatten p-norm minimization (WSNM), and FFDNet, the proposed algorithm achieves a higher peak signal-to-noise ratio, better denoising effect, and visual effects with improved robustness and generalization.

scholarly journals Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization

2020 ◽  
Author(s):  
Xinjian Huang ◽  
Bo Du ◽  
Weiwei Liu
Keyword(s):  
Image Denoising ◽  
Color Image ◽  
Low Rank ◽  
Norm Minimization ◽  
Alternating Direction ◽  
Rank Matrix ◽  
Color Image Denoising ◽  
Non Local ◽  
Comparison Algorithms ◽  
Low Rank Matrix

The R, G and B channels of a color image generally have different noise statistical properties or noise strengths. It is thus problematic to apply grayscale image denoising algorithms to color image denoising. In this paper, based on the non-local self-similarity of an image and the different noise strength across each channel, we propose a MultiChannel Weighted Schatten p-Norm Minimization (MCWSNM) model for RGB color image denoising. More specifically, considering a small local RGB patch in a noisy image, we first find its nonlocal similar cubic patches in a search window with an appropriate size. These similar cubic patches are then vectorized and grouped to construct a noisy low-rank matrix, which can be recovered using the Schatten p-norm minimization framework. Moreover, a weight matrix is introduced to balance each channel’s contribution to the final denoising results. The proposed MCWSNM can be solved via the alternating direction method of multipliers. Convergence property of the proposed method are also theoretically analyzed . Experiments conducted on both synthetic and real noisy color image datasets demonstrate highly competitive denoising performance, outperforming comparison algorithms, including several methods based on neural networks.

scholarly journals A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

2020 ◽  
Vol 12 (14) ◽  
pp. 2336 ◽  
Author(s):  
Shaobo Li ◽  
Jianhu Zhao ◽  
Hongmei Zhang ◽  
Zijun Bi ◽  
Siheng Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Low Rank ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
Non Local ◽  
Better Than ◽  
Total Average

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.

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