Non-blind Image Deconvolution with Adaptive Regularization

Ringing and noise amplification are the most dominant artifacts in image deconvolution. These artifacts can be reduced by introducing image prior into the deconvolution process. A regularization weighting factor can control strength of the regularization. Ringing and noise can be reduced significantly with the strong weighting factor, but details can be lost. We propose a nonblind image deconvolution method with adaptive regularization that can reduce ringing and noise in the smoothed region and preserve image details in the textured region simultaneously. For adaptive regularization, we make a reference image that gives proper edge information and helps to restore a latent image. The reference image guides strength of weighting factor on the pixel of the blurred image. Experimental results show that ringing and noise are suppressed efficiently, while preserving image details.