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

• URL: http://arxiv.org/abs/2006.14780v1
• Date: Fri, 26 Jun 2020 03:34:44 GMT
• Title: Blind Image Deconvolution using Student's-t Prior with Overlapping Group Sparsity
• Authors: In S. Jeon, Deokyoung Kang, Suk I. Yoo
• Abstract summary: A blind image deconvolution problem 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. The proposed method resulted in an effective blind deconvolution algorithm that outperforms other state-of-the-art algorithms.
• Score: 1.4180331276028657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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