Understanding Kernel Size in Blind Deconvolution

TL;DR

This paper analyzes the impact of kernel size in blind deconvolution, revealing issues with oversized kernels, and proposes a low-rank regularization method to improve kernel estimation and deblurring quality.

Contribution

It introduces a theoretical and experimental analysis of kernel size effects and proposes a novel low-rank regularization approach to mitigate errors from large kernels.

Findings

Theoretical analysis of oversized kernel effects

Proposed low-rank regularization improves kernel estimation

Method outperforms existing techniques on benchmark datasets

Abstract

Most blind deconvolution methods usually pre-define a large kernel size to guarantee the support domain. Blur kernel estimation error is likely to be introduced, yielding severe artifacts in deblurring results. In this paper, we first theoretically and experimentally analyze the mechanism to estimation error in oversized kernel, and show that it holds even on blurry images without noises. Then to suppress this adverse effect, we propose a low rank-based regularization on blur kernel to exploit the structural information in degraded kernels, by which larger-kernel effect can be effectively suppressed. And we propose an efficient optimization algorithm to solve it. Experimental results on benchmark datasets show that the proposed method is comparable with the state-of-the-arts by accordingly setting proper kernel size, and performs much better in handling larger-size kernels quantitatively and qualitatively. The deblurring results on real-world blurry images further validate the effectiveness of the proposed method.