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• W2898454416 abstract "Since noise is an inevitable factor in real-world applications, designing robust image recovery technologies is crucial for the application of a compressive sensing (CS) system. However, current CS approaches rarely consider noise, or they only consider the noise generated in the process of transmission. Almost all CS approaches will breakdown when the original image is corrupted by noise due to natural or artificial factors, especially non-Gaussian noise. In this paper, we propose a nonconvex m-estimator-based robust image CS approach to recover the original clean images from the compressed measurements of their corrupted versions. We simultaneously conduct reconstructing and denoising in a unified framework. We also use a weighted schatten-p norm to better exploit the nonlocal low-rank property. In addition, we developed an efficient iterative scheme based on the half-quadratic theory and the alternating direction method of multipliers to solve the resulting nonconvex optimization problem. The experimental results show that the proposed algorithm can recover the clean images accurately when the original images are corrupted by non-Gaussian noise." @default.
• W2898454416 created "2018-11-02" @default.
• W2898454416 creator A5033308848 @default.
• W2898454416 creator A5040537076 @default.
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• W2898454416 date "2019-03-01" @default.
• W2898454416 modified "2023-10-18" @default.
• W2898454416 title "Robust image compressive sensing based on half-quadratic function and weighted schatten-p norm" @default.
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• W2898454416 doi "https://doi.org/10.1016/j.ins.2018.10.050" @default.
• W2898454416 hasPublicationYear "2019" @default.
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