Blurry image can be represented as the convolution of a latent image and a blur kernel, so it is an ill-posed problem to solve the kernel and the latent image inversely from a single blurry image.The most effective way to solving ill-posed problem is using cost function with priori term.For blind image deblurring problem, we propose a ratio of convex norm to concave norm as a regularization priori term, which has more sparse representation ability.When solving the model by variable splitting method, we propose L1 norm fidelity term to update high-frequency information of the latent image.At the stage of updating the blurring kernel, we propose a linear increasing weight parameter to estimate the blurring kernel gradually by multi-scale approach from coarse to fine.After obtaining the blur kernel, we use a closed threshold formula to estimate the latent image.This method can obtain high-quality image efficiently.The experimental results demonstrate the effectiveness of the model and the rapidity of the algorithm.
余义斌, 彭念, 甘俊英. 凹凸范数比值正则化的快速图像盲去模糊[J]. 电子学报, 2016, 44(5): 1168-1173.
YU Yi-bin, PENG Nian, GAN Jun-ying. Fast Blind Image Deblurring Using Ratio of Concave Norm to Convex Norm Regularization. Chinese Journal of Electronics, 2016, 44(5): 1168-1173.
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2016, Vol. 44 
