Single Image Blind Deblurring Based on Salient Edge-Structures and Elastic-Net Regularization

Abstract

In single image blind deblurring, the blur kernel and latent image are estimated from a single observed blurry image. The associated mathematical problem is ill-posed, and an acceptable solution is difficult to obtain without additional priors or heuristics. Inspired by the nonlocal self-similarity in image denoising problem, we introduce elastic-net regularization as a rank prior to improve the estimation of the intermediate image. Furthermore, it is well known that salient edge-structures can provide reliable information for kernel estimation. Therefore, we propose a new blind image deblurring method by combining the salient edge-structures and the elastic-net regularization. The salient edge-structures are selected from the intermediate image and used to guide the estimation of the blur kernel. Then, we employ the elastic-net regularization and edge-structures to further estimate intermediate latent image, by retaining the dominant edge and removing slight texture, for a better kernel estimation. Finally, quantitative and qualitative evaluations are conducted by comparing the results with those obtained by state-of-the-art methods. We conclude that the proposed method performs favorably when considering both synthetic and real blurry images.