21 July 2017 Restoration of single image based on kernel estimation with L1-regularization method
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104201V (2017) https://doi.org/10.1117/12.2282001
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
Image restoration is a significant task in the fields of computer vision and image processing. Image restoration research consists of two aspects: kernel estimation and image restoration. A single image restoration method based on L1-regularized blur kernel estimation is proposed in this paper. First, a bilateral filter is used to remove the image noise effectively. Second, the improved shock filter is used to enhance the edge information of the image. Subsequently, L1-regularization method is used to estimate the blur kernel of the blurred image alternately, during which Split-Bregman algorithm is used to optimize the solution process. Finally, Hyper-Laplacian and sparse priors are applied to the image obtained from the non-blind deconvolution process. Experimental results show that compared to other methods, better restoration results as well as improved computational efficiency can be achieved with the proposed method.
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Minghua Zhao, Hui Cao, Xin Zhang, Zhenghao Shi, Peng Li, "Restoration of single image based on kernel estimation with L1-regularization method", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104201V (21 July 2017); doi: 10.1117/12.2282001; https://doi.org/10.1117/12.2282001
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