Weighted LP -Norm Minimization Algorithm Based on Hybrid Internal and External Priors

Under the Bayesian restoration framework, this paper aims at the problem of the inadequate accuracy of the sparse solution under the traditional convex regularization constraint, which leads to the loss of texture details and excessive edge smoothing in the recovered image, a new method is proposed....

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Bibliographic Details
Published in2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) pp. 1 - 5
Main Authors Sun, Dong, Zhang, Donghua, Ning, Wan, Hu, Yong, Wang, Ru, Gao, Qingwei
Format Conference Proceeding
LanguageEnglish
Published IEEE 31.10.2024
Subjects
dictionary learning
Encoding
group sparsity representation
Image denoising
Image restoration
Machine learning
Mathematical models
Measurement
Minimization
Optimization methods
Smoothing methods
Sparse approximation
weighted l-p norm
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Summary:Under the Bayesian restoration framework, this paper aims at the problem of the inadequate accuracy of the sparse solution under the traditional convex regularization constraint, which leads to the loss of texture details and excessive edge smoothing in the recovered image, a new method is proposed. Firstly, the internal nonlocal self-similarity of the degraded image and the external nonlocal self-similarity in the clean dataset are used to construct similar block groups, which are subject to structured sparse coding, and the sparse coefficients are constrained by a weighted l-p penalty function; Secondly, by combining the sparse coefficients obtained in the previous step, an alternating minimization optimization method is designed to iteratively resolve the image restoration equations for computing a reasonable estimation for the original restored image. Simulation experiments verify the correctness and effectiveness of the proposed scheme.
DOI:10.1109/ICSMD64214.2024.10920562