Blind Poissonian image deconvolution with edge enhancing total variation regularization

被引：0

作者：

Shi Y. ^{[1
,2
]}

Hong H. ^{[1
,2
]}

Hua X. ^{[1
,2
]}

机构：

[1] School of Electrical Information Engineering, Wuhan Institute of Technology, Wuhan

[2] Laboratory for Image Processing and Intelligent Control, Wuhan Institute of Technology, Wuhan

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2016年 / 44卷 / 08期

关键词：

Blind deconvolution; Edge enhance; Edge preserving; Poisson noise; Total variation;

D O I：

10.13245/j.hust.160820

中图分类号：

TN911 [通信理论];

学科分类号：

081002 ;

摘要：

The model of total variation method has certain shortcomings, it favors a piecewise constant solution, which leads to staircase effects in flat regions, and some textures and small details are often diminished with noise. In order to solve this problem of image content, blind Poissonian image deconvolution with edge enhancing total variation regularization method was proposed. Motivated by the fact that edge information plays an important role in visual perception, the edge-enhancing indicator with spatial adaptability was proposed to constrain the total variation regularization, and the bilateral filter was introduced to distinguish flat region and edge information. A series of experiments on simulated and real blurring images show that the proposed method can smooth flat region preferably and has the characteristic of edges preserving. © 2016, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.

引用

页码：94 / 98

页数：4

共 9 条

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