光学精密工程2012，Vol.20Issue(12)：2759-2767,9.DOI:10.3788/OPE.20122012.2759
自适应阈值的超变分正则化图像盲复原
Image blind deblurring based on super total variation regularization with self adaptive threshold
摘要
Abstract
For the serious block effect in first-order variation image blind debluring,an image blind de-blurring method based on super total variation with a self adaptive threshold was proposed to restore the images degraded by unknown Point Spread Function(PSF). Based on the analysis of the total variation model, the super total variation was proposed and the mathematical model of cost function was obtained. The threshold in the model was deduced by estimated image noises. Then, in order to simplify subsequent calculation and improve restoration effect, three auxiliary variables were introduced to transform the cost function into equivalent forms. Finally, semi-quadratic regularization was used to solve iteratively the cost function. The experimental results demonstrate that the restoration image has more details and fewer block effect. Compared with existing blind deblurring methods, the proposed algorithm can increase the Signal to Noise Ratio(SNR) of the restored image by ldB. The restoration effect of the proposed method reveals its practicability in the blind image deblurring.关键词
图像盲复原/超变分正则项/自适应阈值/半二次规整化/辅助变量Key words
blind image deblurring/ super total variation term/ self adaptive threshold/ semi-quadratic regularization/ auxiliary variable分类
信息技术与安全科学引用本文复制引用
周箩鱼,张葆,杨扬..自适应阈值的超变分正则化图像盲复原[J].光学精密工程,2012,20(12):2759-2767,9.基金项目
国家973重点基础研究发展计划资助项目(No.2009CB72400102A) （No.2009CB72400102A）
