Due to the nonlinear ill-posing characteristic of image degradation caused by blur and noise,image restoration is generally difficult.To improve image quality,orthogonal wavelet was taken as sparse basis and the sparsity of wavelet coefficients as prior.By constructing a convex function and minimizing this function,we obtained the recovered image.The minimization problem was transferred into a proximal operator which is solved by the fixed point theory.We proved that the optimal solution can be obtained through repeatedly iterating an analytical formula.The construction process,convergence rate and complexity of the method were discussed,and an accelerated algorithm was presented.Simulation results indicate that our method can remove blur and noise,and keep detail information meanwhile.
王斌, 胡辽林, 曹京京, 薛瑞洋, 刘光飞. 基于小波域稀疏最优的图像修复方法[J]. 电子学报, 2016, 44(3): 600-606.
WANG Bin, HU Liao-lin, CAO Jing-jing, XUE Rui-yang, LIU Guang-fei. Image Restoration Based on Sparse-Optimal Strategy in Wavelet Domain. Chinese Journal of Electronics, 2016, 44(3): 600-606.
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2016, Vol. 44 
