Kernel Estimation from Salient Structure for Robust Motion Deblurring

TL;DR

This paper introduces a novel kernel estimation method from a single blurred image that leverages TV-based structure computation, gradient selection, and adaptive priors to improve motion deblurring, especially in large blur cases.

Contribution

It proposes a new kernel estimation approach that effectively removes image details caused by blurring, enhancing robustness in challenging deblurring scenarios.

Findings

Improved kernel estimation accuracy in large blur cases

Enhanced robustness against salient edges during kernel estimation

Effective preservation of sharp edges in latent image restoration

Abstract

Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good kernel estimate from a single blurred image based on the image structure. We found that image details caused by blurring could adversely affect the kernel estimation, especially when the blur kernel is large. One effective way to eliminate these details is to apply image denoising model based on the Total Variation (TV). First, we developed a novel method for computing image structures based on TV model, such that the structures undermining the kernel estimation will be removed. Second, to mitigate the possible adverse effect of salient edges and improve the robustness of kernel estimation, we applied a gradient selection method. Third, we proposed a novel kernel estimation method, which is capable of preserving the continuity and sparsity of the kernel and reducing the noises. Finally, we developed an adaptive weighted spatial prior, for the purpose of preserving sharp edges in latent image restoration. The effectiveness of our method is demonstrated by experiments on various kinds of challenging examples.