Title: Understanding kernel size in blind deconvolution,
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, and is proportional to kernel size. In this paper, we
experimentally and theoretically show the reason of noises introduction in
oversized kernel by demonstrating that sizeable kernels lead to lower
optimization cost. To eliminate this adverse effect, we propose a low
rank-based regularization on blur kernel by analyzing the structural
information in degraded kernels. Compared with the sparsity prior, e.g.,
$\ell_\alpha$-norm, our regularization term can effectively suppress random
noises in oversized kernels. On benchmark test dataset, the proposed method is
compared with several state-of-the-art methods, and can achieve better
quantitative score. Especially, the improvement margin is much more significant
for oversized blur kernels. We also validate the proposed method on real-world
blurry images.