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Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising
Jiucheng Xu1,2, Yihao Cheng1,2, Yuanyuan Ma1,2
1College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.
Entropy (Basel, Switzerland)
|January 30, 2021
Summary
This study introduces a novel image denoising algorithm that incorporates non-local self-similarity errors. This approach enhances low-rank matrix restoration for superior image quality and robustness.
Area of Science:
- Computer Vision
- Image Processing
- Matrix Restoration
Background:
- Traditional image denoising often overlooks non-local self-similarity errors.
- Existing low-rank matrix restoration methods may not fully capture image intricacies.
Purpose of the Study:
- To develop an advanced image denoising algorithm.
- To integrate non-local self-similarity errors into low-rank matrix restoration.
Main Methods:
- Introduced non-local self-similarity error into the weighted Schatten p-norm minimization model.
- Constrained low-rank error using Schatten p-norm for improved matrix restoration.
Main Results:
- Achieved higher peak signal-to-noise ratio (PSNR) compared to BM3D, WNNM, WSNM, and FFDNet.
- Demonstrated superior denoising effects and visual quality on classic datasets.
- Exhibited improved robustness and generalization capabilities.
Conclusions:
- The proposed algorithm effectively addresses limitations of traditional denoising methods.
- Integrating non-local self-similarity errors enhances low-rank matrix restoration for image denoising.
- The method offers a promising advancement in image denoising performance.
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