It has been proven that structure of image play an important role in kernel estimation.In recent years,many successful algorithms propose to generate intermediate image by extracting structure from latent image,and then use it for blur kernel estimation.However,these methods ignore to extract the correspondence from input blurry image.This will cause unbalanced data item of objective function.In this paper we first exploit a mask determined by convolution of intermediate image with kernel to generate the correspondence,and then take it into data item instead of blurry image to overcome the problem.Moreover,we have found that kernel shows the properties of sparse both in intensity domain and derivatives domain.Accordingly,we apply L0-norm regularization to constrain both intensity domain and derivatives domain of kernel.Compared with the state-of-the-art algorithms,experiments across datasets showed that our algorithm achieved better performance.%图像结构边缘对模糊核估计有重要意义.近年来许多成功的算法都致力从潜在清晰图像中分离出结构边缘形成中间图像,然后用其与模糊图像一起估计模糊核.但是这些算法忽视了从模糊图像中分离出结构边缘对应的部分,导致核估计过程中目标函数的数据项不平衡.针对这一问题,本文利用中间图像和潜在模糊核产生二值模板对模糊图像进行处理,分离出结构边缘对应的部分,并用其修正目标函数.此外本文提出采用L0范数同时约束幅值域和梯度域的正则项,从而缩小核估计的解空间.多个标准测试数据库上实验结果表面,本文算法无论在鲁棒性还是准确性方面均具有更好的效果.
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