scholarly journals NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 167-170
Author(s):  
A. V. Nasonov ◽  
O. S. Volodina ◽  
A. S. Krylov
Keyword(s):  
Image Denoising ◽  
Super Resolution ◽  
Low Noise ◽  
Block Matching ◽  
Norm Minimization ◽  
Noise Image ◽  
Multiple Input ◽  
Noticeable Improvement ◽  
Repeated Structure ◽  
Weighted Nuclear Norm Minimization

Abstract. We address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images. Unlike traditional multi-frame super-resolution algorithms, the block-matching approach does not require computationally expensive motion estimation for multi-frame image denoising. In this work, we use an algorithm based on weighted nuclear minimization for image denoising. The evaluation of the algorithm shows noticeable improvement of image quality when using multiple input images instead of single one. The improvement is the most noticeable in the areas with complex non-repeated structure.

Image Denoising Using Weighted Nuclear Norm Minimization with Preserving Local Structure

2019 ◽  
Vol 56 (16) ◽  
pp. 161006
Author(s):  
吕俊瑞 Junrui Lü ◽  
罗学刚 Xuegang Luo ◽  
岐世峰 Shifeng Qi ◽  
彭真明 Zhenming Peng
Keyword(s):  
Image Denoising ◽  
Local Structure ◽  
Nuclear Norm ◽  
Nuclear Norm Minimization ◽  
Norm Minimization ◽  
Weighted Nuclear Norm Minimization

Multi-weighted nuclear norm minimization for real world image denoising

Optik ◽  
2020 ◽  
Vol 206 ◽  
pp. 164214
Author(s):  
Xue Guo ◽  
Feng Liu ◽  
Jie Yao ◽  
Yiting Chen ◽  
Xuetao Tian
Keyword(s):  
Image Denoising ◽  
Real World ◽  
Nuclear Norm ◽  
Nuclear Norm Minimization ◽  
Norm Minimization ◽  
Weighted Nuclear Norm Minimization ◽  
World Image

Image denoising using weighted nuclear norm minimization with multiple strategies

2017 ◽  
Vol 135 ◽  
pp. 239-252 ◽  
Author(s):  
Xiaohua Liu ◽  
Xiao-Yuan Jing ◽  
Guijin Tang ◽  
Fei Wu ◽  
Qi Ge
Keyword(s):  
Image Denoising ◽  
Nuclear Norm ◽  
Nuclear Norm Minimization ◽  
Multiple Strategies ◽  
Norm Minimization ◽  
Weighted Nuclear Norm Minimization

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 Improved image denoising via RAISR with fewer filters

2021 ◽  
Author(s):  
Theingi Zin ◽  
Yusuke Nakahara ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
High Performance ◽  
Computational Cost ◽  
Super Resolution ◽  
Ground Truth ◽  
Noise Removal ◽  
Block Matching ◽  
Additional Time ◽  
Denoised Image

AbstractIn recent years, accurate Gaussian noise removal has attracted considerable attention for mobile applications, as in smart phones. Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time. Therefore, we propose a rapid post-processing method for Gaussian noise removal in this paper. Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise. Although these nonlocal image denoising methods have quantitatively high performance, some fine image details are lacking due to the loss of high frequency information. To tackle this problem, an improvement to the pioneering RAISR approach (rapid and accurate image super-resolution), is applied to rapidly post-process the denoised image. It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost, preserving important image structures well. Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements: geometric conversion and reduction of the strength classes. In addition, following RAISR, the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results. Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods, efficiently and with low memory requirements.

scholarly journals Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 655
Author(s):  
Run Tian ◽  
Guiling Sun ◽  
Xiaochao Liu ◽  
Bowen Zheng
Keyword(s):  
Edge Detection ◽  
Image Denoising ◽  
Detection Algorithm ◽  
Nuclear Norm ◽  
Nuclear Norm Minimization ◽  
Processing Technologies ◽  
Sobel Operator ◽  
Norm Minimization ◽  
Sobel Edge Detection ◽  
Weighted Nuclear Norm Minimization

As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity.

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