Parametric model for image blur kernel estimation

TL;DR: This paper proposes an novel parametric approach for single image kernel estimation with both motion blur and Gaussian blur coupled, and shows that the proposed method outperforms others when the blur kernel is fairly parameterized, which helps the current blind deconvolution methods achieve better results.

Abstract: This paper we propose an novel parametric approach for single image kernel estimation with both motion blur and Gaussian blur coupled. In the view of that daily pictures captured by handheld device usually contain motion blur and defocus simultaneously. During one shot, the moving trail of the object can be always regarded as straight and consecutive, and the defocus phenomenon is related to Gaussian blur. Therefore, a parameter model containing three parameters can describe the blur. First, we estimate a rough blur kernel using L 1 prior method, then we fit the kernel by computing the three parameters. Finally, the sharp image with clear details is restored by the kernel estimated. Experimental results show that the proposed method outperforms others when the blur kernel is fairly parameterized, which helps the current blind deconvolution methods achieve better results.