Salient edges combined with image structures for image deblurring.

This work investigates a simple yet effective image deblurring method that combines salient image edges with structures. Typically, the vast majority of edge-based work focuses on advancing the salient edges information for blind image deblurring while ignoring the refined scale of the image structures. With this in mind, we show how to effectively exploit the image structure to blind image deblurring in this paper, i.e., we adopt a mutually guided image filter to guide the restoration of the image structure while salient edges provide edge information for blur kernel estimation. Thus, a more expressive deblurring model that employs L 2 -regularization for salient edges and image structure, together with the L 0 -regularization for image gradients is proposed. We find that the quality of the recovered kernel is thereby improved, and the deblurring results are more satisfactory. Experimental results show that our method outperforms state-of-the-art methods in terms of benchmark-based datasets and real scenarios, as well as computational efficiency. • We develop a novel structure-based deblurring term to provide guidance information for image structures. • We build a more expressive model with image structure and salient edges. • We incorporate the L 0 -norm of the gradient for edge-preserving and detail removal. • Quantitative and qualitative evaluations show that our method is comparable to state-of-the-art methods. [ABSTRACT FROM AUTHOR]