PSF estimation using total variation regularization and shock filter for blind deconvolution

Hiroki Senshiki, Satoshi Motohashi, Tomio Goto, Haifeng Chen, Reo Aoki

Abstract: Blind deconvolution, which restores a clear ideal image from a single blur image, is the ill-posed problem of finding two unknowns: the point spread function (PSF) and the ideal image. In this paper, we propose a novel PSF estimation using total variation regularization and the shock filter. The experimental results show that our method is the best of several methods with respect to deblurring performance. Furthermore, our proposed method creates high quality images for consumer cameras and smartphones with a camera.