电子学报2016，Vol.44Issue(5)：1168-1173,6.DOI:10.3969/j.issn.0372-2112.2016.05.022
凹凸范数比值正则化的快速图像盲去模糊
Fast Blind Image Deblurring Using Ratio of Concave Norm to Convex Norm Regularization
摘要
Abstract
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 ef-fectiveness of the model and the rapidity of the algorithm.关键词
凹凸范数比值正则化/图像盲去模糊/变量分裂法/封闭阈值Key words
ratio of concave norm to convex norm regularization/blind image deblurring/variable split method/closed-form threshold分类
信息技术与安全科学引用本文复制引用
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):1168-1173,6.基金项目
广东高校省级重点平台和重大科研项目特色创新项目（自然科学类）（No．2015KTSCX148）；浙江省信号处理重点实验室开放课题（No．ZJKL －4－SP-OP2014-05）；国家自然科学基金 （）
