Single image motion deblurring using anisotropic regularization
Hanyu Hong, In Kyu Park · 2010
For motion deblurring from a single blurred image, it is of utmost importance to estimate the blur kernel accurately. In this paper, we propose a new anisotropic regularization blur kernel estimation algorithm which preserves the point spread function (PSF) path while keeping the properties of motion PSF into seeking the solution of the blur kernel. In order to preserve the PSF path, the nonquadratic regularization and the refinement of the blur kernel are incorporated in the iterative process to improve the precision of the blur kernel. A single motion blurred image can be restored well after the accurate motion PSF is estimated. Experimental results demonstrate that the proposed approach is efficient and effective to reduce motion blur with arbitrary direction in a single image.