重加权总变分结合hyper-Laplacian的图像盲复原方法  

Blind Image Restoration Method Based on Reweighted Graph Total Variation and Hyper-Laplacian

作　　者：许泽海 宋海燕[1] Xu Zehai;Song Haiyan(College of Engineering,Shanxi Agricultural University,Jinzhong,Shanxi 030801,China)

机构地区：[1]山西农业大学工学院,山西晋中030801

出　　处：《激光与光电子学进展》2020年第8期218-226,共9页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2018YFD0700300)。

摘　　要：提出一种重加权总变分与hyper-Laplacian相结合的图像盲复原算法。首先,通过重加权总变分先验重建模糊图像权重的双峰分布;然后,利用重建后的图像估计连续且稀疏分布的点扩展函数,并用其复原模糊图像,对以上两步反复迭代,使点扩展函数不断接近真实的解;最后,结合hyper-Laplacian函数曲线能很好地拟合自然图像梯度分布的先验对模糊图像进行非盲复原。实验结果表明,与两种具有代表性的盲复原算法相比,该算法能更准确地预测出模糊核,并有效抑制图像的振铃效应,且在主观视觉与客观评价指标上都得到明显的提升。In this paper,a blind image restoration algorithm based on reweighted graph total variation combined with hyper-Laplacian is proposed.First,the bimodal distribution of the weight of a blurred image is reconstructed using the reweighted graph total variation.Next,the reconstructed image is used to estimate the continuity and sparsity of the point spread function(PSF)and the blurred image is restored by the PSF.These two processes are repeatedly iterated to make the PSF approach the ideal solution continuously.Finally,we combined it with apriori,that is,the hyper-Laplacian cave,which can best fit a natural image gradient distribution to achieve the non-blind restoration of the blurred image.Experimental results show that the proposed algorithm can give a more accurate prediction of the blurred kernel and effectively reduce the ringing effect in images compared with two representative blind restoration algorithms developed in recent years.Moreover,there is an improvement in subjective vision and objective elevation indicators.

关 键 词：图像处理 图像盲复原 重加权总变分 hyper-Laplacian函数 模糊核 迭代 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]