Coded exposure imaging for projective motion deblurring

Yu-Wing Tai, Naejin Kong, Stephen Lin, Sung Yong Shin

Abstract: We propose a method for deblurring of spatially variant object motion. A principal challenge of this problem is how to estimate the point spread function (PSF) of the spatially variant blur. Based on the projective motion blur model of, we present a blur estimation technique that jointly utilizes a coded exposure camera and simple user interactions to recover the PSF. With this spatially variant PSF, objects that exhibit projective motion can be effectively de-blurred. We validate this method with several challenging image examples.