Image deblurring based on enhanced salient edge selection

Blind image deblurring is a severely ill-posed problem in low-level vision. The success of blind image deblurring relies on statistical priors and well-designed regularizers to obtain a clear image. However, the prior-based method is time-consuming due to a lot of nonlinear calculations. To improve efficiency, this work proposes an enhanced salient edge selection for blind image deblurring. Different from the previous methods that only focus on the salient edge and ignore the image structure, the image sharpening operator is adopted to guide the finer image structure when salient edges provide strong edge information for blur kernel estimation. We find that the latent image restoration can be enhanced by joining the salient edge selection and an image sharpening operator, and the quality of the recovered kernel is hereby improved. By imposing L-2 constraints on the salient edge and sharpening operator terms, a new energy function is introduced and an effective alternating optimization strategy is explored. Extensive experiments have been conducted, which shows that our method is more effective compared with state-of-the-art methods.