Non-blind image deblurring method by local and nonlocal total variation models

Abstract

Although the total variation (TV) model can preserve the salient edges of the image, it smoothes out the image details. To preserve the salient edges while restoring the image details effectively, in this paper, we propose a new non-blind image deblurring (NBID) method, which combines the TV and the nonlocal total variation (NLTV) models. First, the original image is decomposed into three components: salient edges, details, and constant regions by a global gradient extraction scheme (GGES). Second, the TV model is applied on the salient edges and constant regions, and the NLTV model is applied on the details. At last, a split Bregman based multi-variable minimization (SBMM) iterative scheme is employed to optimize the proposed NBID inverse problem. Experiments demonstrate the viability and efficiency of the proposed method in terms of subjective vision, peak signal-to-noise ratio (PSNR), and self-similarity measure (SSIM).