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Article Dans Une Revue Journal of Visual Communication and Image Representation Année : 2018

A note on patch-based low-rank minimization for fast image denoising

Résumé

Patch-based low-rank minimization for image processing attracts much attention in recent years. The minimization of the matrix rank coupled with the Frobenius norm data fidelity can be solved by the hard thresholding filter with principle component analysis (PCA) or singular value decomposition (SVD). Based on this idea, we propose a patch-based low-rank minimization method for image denoising. The main denoising process is stated in three equivalent way: PCA, SVD and low-rank minimization. Compared to recent patch-based sparse representation methods, experiments demonstrate that the proposed method is rather rapid, and it is effective for a variety of natural grayscale images and color images, especially for texture parts in images. Further improvements of this method are also given. In addition, due to the simplicity of this method, we could provide an explanation of the choice of the threshold parameter, estimation of PSNR values, and give other insights into this method.
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Dates et versions

hal-01707218 , version 1 (12-02-2018)

Identifiants

Citer

Haijuan Hu, Jacques Froment, Quansheng Liu. A note on patch-based low-rank minimization for fast image denoising. Journal of Visual Communication and Image Representation, 2018, 50, pp.100 - 110. ⟨10.1016/j.jvcir.2017.11.013⟩. ⟨hal-01707218⟩
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