  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.
