Joint blur kernel estimation and CNN for blind image restoration

Abstract

Convolutional neural networks (CNN) have shown its excellent performance in computer vision fields. Recently, they are successfully applied to image restoration. This paper proposes a joint blur kernel estimation and CNN method for blind image restoration. The blur kernel estimation is based on both blur support parameter estimation and blur type identification. An automatic feature line detection algorithm is presented for blur support parameter estimation and a dictionary learning algorithm is presented for the blur type identification. Once the blur kernel estimate is obtained, we use an effective CNN for iterative non-blind deconvolution, which is able to automatically learn image priors. Compared with current blind image restoration methods, the proposed joint method can obtain restored images under three types of unknown blur kernels. The experimental result shows that the proposed blur kernel estimation algorithm can provide high accuracy results. Furthermore, the proposed joint blur kernel estimation and CNN algorithm is superior to conventional blind image restoration algorithms in terms of restoration quality and computation time.