Non-blind image deconvolution using a regularization based on re-blurring process

Image deconvolution is an ill-posed problem that requires a regularization term to solve. The most common forms of image priors used as the regularization term in image deconvolution tend to produce smoothed (slightly blurry) images, hence don’t well reconstruct image details. In this paper, a new image prior is introduced. This prior is used as a regularization term to model the non-blind image deconvolution problem. The complete regularization term consists of two parts: one that penalizes non-sparsity of the gradient, and the other that penalizes the blurriness in the image. The resulting regularization term favors sharp images over blurry ones. It means that the minimum of this term corresponds to the true sharp solution. The minimization problem is non-convex and we propose a scheme based on half-quadratic regularization. We demonstrate the efficiency of the proposed method by performing several quantitative and qualitative comparisons with the existing non-blind image deconvolution methods for deblurring artificial and real blurred images. The experimental results show that the proposed method tends to well reconstruct image details whilst suppresses noise. In addition, the reconstructed images have a higher PSNR and a lower blurriness value compared to those in the existing methods.