Understanding Kernel Size in Blind Deconvolution
This addresses a specific issue in image deblurring for computer vision applications, offering an incremental improvement over existing methods.
The paper tackles the problem of kernel size selection in blind deconvolution, where pre-defined large kernels cause estimation errors and artifacts; the proposed low rank-based regularization method suppresses these effects, achieving comparable or better performance than state-of-the-art methods on benchmark datasets and real-world images.
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, yielding severe artifacts in deblurring results. In this paper, we first theoretically and experimentally analyze the mechanism to estimation error in oversized kernel, and show that it holds even on blurry images without noises. Then to suppress this adverse effect, we propose a low rank-based regularization on blur kernel to exploit the structural information in degraded kernels, by which larger-kernel effect can be effectively suppressed. And we propose an efficient optimization algorithm to solve it. Experimental results on benchmark datasets show that the proposed method is comparable with the state-of-the-arts by accordingly setting proper kernel size, and performs much better in handling larger-size kernels quantitatively and qualitatively. The deblurring results on real-world blurry images further validate the effectiveness of the proposed method.