Blind Image Deconvolution using Student's-t Prior with Overlapping Group Sparsity
This addresses the problem of removing blurs from degraded images without prior knowledge of the blur kernel for applications in image processing, representing an incremental improvement over existing methods.
The paper tackled blind image deconvolution by proposing a new formulation that combines Student's-t prior with overlapping group sparsity to leverage structural information in sparse coefficients, resulting in an algorithm that outperforms state-of-the-art methods.
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.