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 amo...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.展开更多
文摘本文提出了一种基于圆边响应曲线的离焦估计新算法,实现了调焦参数的标定和望远物镜的自动调焦。建立了圆形边缘的灰度响应与离焦半径的关系模型,设计了圆四周离焦半径的双阈值均值滤波器,实现了模糊图像离焦半径的精确估计,降低了运动模糊和噪声的影响。根据离焦半径与调焦距离的线性关系,采用折线拟合方法,求解正焦调焦距离。然后,利用多个物距和正焦像距,优化求解测距调焦模型参数,实现了成像系统的自动调焦。仿真和实际实验验证了离焦半径估计算法的有效性和鲁棒性。标定后的自动调焦成像系统图像清晰,拍摄距离在43~52 m之间的物理分辨率接近理论值的一半,可分辨线宽优于0.354 mm.
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)。
文摘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.
