基于稀疏正则优化的图像复原算法

doi:10.3724/SP.J.1087.2012.00261

计算机应用 ›› 2012, Vol. 32 ›› Issue (01): 261-263.DOI: 10.3724/SP.J.1087.2012.00261

基于稀疏正则优化的图像复原算法

肖宿¹,韩国强²

1. 淮北师范大学计算机科学与技术学院，安徽淮北 235000
2. 华南理工大学计算机科学与工程学院，广州 510006

收稿日期:2011-08-01 修回日期:2011-09-22 发布日期:2012-02-06 出版日期:2012-01-01
通讯作者: 肖宿
作者简介:肖宿(1982-)，男，安徽淮北人，讲师，博士，主要研究方向：数字图像复原；韩国强(1962-)，男，江西临川人，教授，博士生导师，博士，主要研究方向：数字图像处理。
基金资助:
国家自然科学基金资助项目(61070090);国家自然科学基金青年基金资助项目(61102117);淮北师范大学青年科研项目(700442)

Image restoration algorithm based on sparse regularized optimization

XIAO Su¹,HAN Guo-qiang²

1. School of Computer Science and Technology, Huaibei Normal University, Huaibei Anhui 235000, China
2. School of Computer Science and Engineering, South China University of Technology, Guangzhou Guangdong 510006, China

Received:2011-08-01 Revised:2011-09-22 Online:2012-02-06 Published:2012-01-01
Contact: XIAO Su

摘要/Abstract

摘要： 为提高图像复原的速度，改进图像复原的质量，提出一种新算法。将图像复原表示为一类标准的优化问题，采用交替最小化把该优化问题分解为等价的两个子问题。通过迭代求解这两个子问题，获得图像复原问题的解。在此迭代过程中，引入迭代软阈值法处理图像降噪子问题。实验对不同类型的模糊图像进行了复原，其结果验证了算法的有效性。与多级阈值Landweber(MLTL)算法和快速收缩阈值算法(FISTA)相比，处理相同图像时，所提算法可分别节省28%和71%的时间，同时复原图像的信噪比(SNR)可提高0.7~3.5dB。

关键词: 图像复原, 约束优化问题, 稀疏表示, 交替最小化, 迭代软阈值

Abstract: For speeding up image restoration and improving the restored results, a new algorithm was proposed. The image restoration was represented as a class of standard optimization problem, which was decomposed into two subproblems by the alternating minimization algorithm. By iteratively solving the two subproblems, a solution to the image restoration problems was obtained. During the subproblem solving, the iterative soft-thresholding algorithm was introduced for the denoising subproblem. In the experiment, the images blurred by different type of blur were restored. The experimental results show the effectiveness of the proposed algorithm. When dealing with the images, compared with Multilevel Thresholded Landweber (MLTL) and Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), the proposed algorithm can reduce the time by 28% and 71% respectively, and it improves the Signal-to-Noise Ratio (SNR) values by 0.7dB to 3.5dB.

Key words: image restoration, constrained optimization problem, sparse representation, alternating minimization, iterative soft-thresholding

中图分类号:

TP391.413

肖宿韩国强. 基于稀疏正则优化的图像复原算法[J]. 计算机应用, 2012, 32(01): 261-263.

XIAO Su HAN Guo-qiang. Image restoration algorithm based on sparse regularized optimization[J]. Journal of Computer Applications, 2012, 32(01): 261-263.

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基于稀疏正则优化的图像复原算法

Image restoration algorithm based on sparse regularized optimization

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