COMBINED PATCH-WISE MINIMAL-MAXIMAL PIXELS REGULARIZATION FOR DEBLURRING

Han, J.; Zhang, S. L.; Ye, Z.

Deblurring is a vital image pre-processing procedure to improve the quality of images. It is a classical ill-posed problem. A new blind deblurring method based on image sparsity prior is proposed here. The proposed image sparsity prior combines patch-wise minimal and maximal pixels of latent image, and improves gradually the image sparsity during deblurring. An algorithm that is different with half quadratics splitting algorithm is applied under the maximum a posterior (MAP) framework. Experiment results demonstrate that the proposed method can keep more subtle texture and sharpened edges, reduce the artefacts in visual, and the corresponding evaluated indexes perform favourably against it of the state-of-the-art methods on synthesized, natural and remote sensing images (RSI) quantitatively.

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Han, J. / Zhang, S. L. / Ye, Z.: COMBINED PATCH-WISE MINIMAL-MAXIMAL PIXELS REGULARIZATION FOR DEBLURRING. 2020. Copernicus Publications.

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