空间变化PSF图像复原技术的研究现状与展望  

Review and Prospect of Image Restoration with Space-variant Point Spread Function

作　　者：冯华君[1] 陶小平[1] 赵巨峰[1] 李奇[1] 徐之海[1] 

机构地区：[1]浙江大学光电系,杭州310027

出　　处：《光电工程》2009年第1期1-7,共7页Opto-Electronic Engineering

基　　金：国家高科技研究与发展计划(863)资助项目(2006AA12Z107);国家重点基础研究发展计划(973)2009CB724006

摘　　要：空间变化PSF(Space-variant Point Spread Function,SVPSF)图像,即物空间各点的退化随位置的改变而改变的图像,由于其复原技术涉及到多个甚至海量PSF的提取、存储和运算,相对于空间不变PSF(Space-Invariant Point Spread Function,SIPSF)图像复原要困难得多。目前处理此类图像的主要方法包括空间坐标转换法,等晕区分块复原法,以减少数据存储量,降低计算量,提高收敛速度为目标的直接复原法等。本文回顾了这一课题的研究历史,对目前的研究工作进行了分析和总结,介绍了本实验室提出的结合GRM(Gradient Ringing Metric)评价算法的总变分最小化图像分块复原法,并提出了未来工作关注重点的展望。Compared with the image with Space-invariant Point Spread Function （SVPSF）, the restoration of the image with SVPSF ,whose degradation function in the object domain is different from point to point, is more difficult because of the extraction, storage and calculation of massive PSFs. Fortunately, three methods have been proposed to solve the problems： coordinate transformation approaches, sectioned restoration methods and direct algorithms, which are aimed to reduce the data storage, decrease the computational complexity, and improve convergence rate. The paper summarizes the research on the reconstruction of the image with SVPSF in the history, and then points out the crucial problems of the development of the issue in the future. Moreover, a total variation majorization-minimization sectioned restoration algorithm based on gradient ringing metric image quality assessment is exhibited.

关 键 词：图像复原 空间变化PSF 空间坐标转换法 分块复原法 总变分最小化算法 

分 类 号：TN911.73[电子电信—通信与信息系统]