摘要
本文提出一种新的结构字典学习方法,并利用它进行图像复原。首先给出结构字典学习的基本内容和方法,然后将傅里叶正则化方法和结构字典学习方法有效整合到图像复原算法中。结构字典学习方法是先将原图像进行结构分解,再分别学习出每个结构图像中的字典,最后利用这些字典对原图像进行稀疏的表示。结合傅里叶正则化,提出了一种有效的迭代图像复原算法:第一步在傅里叶域利用正则化反卷积方法得到图像的初步估计;第二步采用结构字典学习的方法对遗留的噪声进行去噪处理。实验结果表明,提出的方法在改进信噪比和视觉质量上都要优于6种先进的图像复原方法,改进的信噪比平均提升0.5 d B以上。
In this paper,we propose a new structure dictionary learning method,and perform image restoration based on this approach. First,we define the structure dictionary for the nature image. Second,an iterative algorithm is proposed with the decouple of deblurring and denoising steps in the restoration process,which effectively integrates the Fourier regularization and structure dictionary learning technique into the deconvolution framework. Specifically,we propose an iterative algorithm. In the deblurring step,we involve a regularized inversion of the blur in Fourier domain. Then we remove the remained noise using the structure dictionary learning method in the denoising step. Experiment results show that this approach outperforms 6 state-of-theart image deconvolution methods in terms of improvement signal to noise rate（ ISNR） and visual quality,and the ISNR can be improved by more than 0. 5 dB.
作者
杨航
吴笑天
王宇庆
YANG Hang WU Xiao-tian WANG Yu-qing(Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China)
出处
《中国光学》
EI
CAS
CSCD
2017年第2期207-218,共12页
Chinese Optics
基金
国家自然科学基金资助项目(No.61401425)~~
关键词
结构字典
字典学习
图像复原
反卷积
structure dictionary
dictionary learning
image restoration
deconvolution
