The application of low rank matrix decomposition method in image restoration

Yue Peng

doi:10.1117/12.2659313

22 November 2022 The application of low rank matrix decomposition method in image restoration

Yue Peng

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124750F (2022) https://doi.org/10.1117/12.2659313
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

Image restoration is a hot issue in the field of image processing. Traditional algorithms approach the original function by a regular function or remove redundant noise by similarity. This process is complicated and cumbersome. In this paper, the low rank approximate matrix of the image matrix is equivalent to the product of two smaller matrices. At the same time, the first-order and second-order statistical information of the image matrix is effectively maintained by using the matrix Frobenius norm and matrix kernel normal. Secondly, the alternating direction multiplier method is utilized to solve the model. Finally, experimental results test the effectiveness of the proposed algorithm.

Citation Download Citation

Yue Peng "The application of low rank matrix decomposition method in image restoration", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124750F (22 November 2022); https://doi.org/10.1117/12.2659313

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KEYWORDS

Image restoration

Image processing

Image quality

Signal to noise ratio

Convex optimization

Image transmission

Distortion

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