Blind Image Deconvolution using Student's-t Prior with Overlapping Group Sparsity

TL;DR

This paper introduces a novel blind image deconvolution method that combines Student's-t prior with overlapping group sparsity to better utilize structural information, leading to improved deblurring performance.

Contribution

It proposes a new formulation for blind deconvolution that integrates structural sparsity with Student's-t prior, enhancing deblurring accuracy over existing methods.

Findings

Outperforms state-of-the-art blind deconvolution algorithms.

Effectively captures structural information in sparse coefficients.

Results demonstrate improved image restoration quality.

Abstract

In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domains are sparse. However, it is assumed here that there exist additional structures over the sparse coefficients. Accordingly, we propose new problem formulation for the blind image deconvolution, which utilizes the structural information by coupling Student's-t image prior with overlapping group sparsity. The proposed method resulted in an effective blind deconvolution algorithm that outperforms other state-of-the-art algorithms.