Image defocus deblurring method based on gradient difference of boundary neighborhood  

作　　者：Junjie TAO Yinghui WANG Haomiao MA Tao YAN Lingyu AI Shaojie ZHANG Wei LI 

机构地区：[1]School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China [2]School of Computer Science,Shaanxi Normal University,Xi’an 710119,China [3]School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China

出　　处：《Virtual Reality & Intelligent Hardware》2023年第6期538-549,共12页虚拟现实与智能硬件（中英文）

基　　金：Supported by the National Natural Science Foundation of China (62172190);the“Double Creation”Plan of Jiangsu Province (JSSCRC2021532);the“Taihu Talent-Innovative Leading Talent”Plan of Wuxi City (Certificate Date:202110)。

摘　　要：Background For static scenes with multiple depth layers,existing defocused image deblurring methods have the problems of edge-ringing artifacts or insufficient deblurring owing to inaccurate estimation of the blur amount,and prior knowledge in nonblind deconvolution is not strong,which leads to image detail recovery challenges.Methods To this end,this study proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood,which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring,thereby preventing boundary ringing artifacts.The obtained blur map is then used for blur detection to determine whether the image needs to be deblurred,thereby improving the efficiency of deblurring without manual intervention and judgment.Finally,a nonblind deconvolution algorithm was designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.Results Experimental results showed that our method improves PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)by an average of 4.6%and 7.3%,respectively,compared to existing methods.Conclusions Experimental results showed that the proposed method outperforms existing methods.Compared to existing methods,our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

关 键 词：Defocused image DEBLURRING GRADIENT Boundary neighborhood Blur amount estimation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]