Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4977591 | Signal Processing | 2017 | 41 Pages |
Abstract
Image deconvolution is still to be a challenging ill-posed problem for recovering a clear image from a given blurry image, when the point spread function is known. Although competitive deconvolution methods are numerically impressive and approach theoretical limits, they are becoming more complex, making analysis, and implementation difficult. Furthermore, accurate estimation of the regularization parameter is not easy for successfully solving image deconvolution problems. In this paper, we develop an effective approach for image restoration based on one explicit image filter - guided filter. By applying the decouple of denoising and deblurring techniques to the deconvolution model, we reduce the optimization complexity and achieve a simple but effective algorithm to automatically compute the parameter in each iteration, which is based on Morozov's discrepancy principle. Experiments manifest that our algorithm is effective on finding a good regularization parameter, and the proposed algorithm outperforms many competitive methods in both ISNR and visual quality.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Hang Yang, Zhongbo Zhang, Yujing Guan,