Blind Image Deblurring Using Patch-Wise Minimal Pixels Regularization.
Fei Wen, Rendong Ying, Peilin Liu, Trieu‐Kien Truong · 2019
Blind image deblurring is a long standing challenging problem in image processing and low-level vision. Recently, sophisticated priors such as dark channel prior, extreme channel prior, and local maximum gradient prior, have shown promising effeciveness. However, these methods are computationally expensive. Meanwhile, since these priors involved subproblems cannot be solve explicitly, non-rigorous approximation is commonly used, which limits the best exploitation of their capability. To address these problems, this work firstly proposes a simplified sparsity prior of local minimal pixels, namely patch-wise minimal pixels (PMP). The PMP of clear images is much more sparse than that of blurred ones, and hence is very effective in discriminating between clear and blurred images. Then, a novel algorithm is designed to efficiently exploit the sparsity of PMP in deblurring. The new algorithm flexibly imposes sparsity promotion on the PMP under the MAP framework, which avoids non-rigorous approximation in existing algorithms while being more computationally efficient. Extensive experiments demonstrate that the proposed algorithm can achieve state-of-the-art performance on both natural and specific images. In terms of both deblurring quality and computational efficiency, the new algorithm is superior to state-of-the-art methods. Code for reproducing the results of the new method is available at https://github.com/FWen/deblur-pmp.git.