Cepstral analysis based blind deconvolution for motion blur

Camera shake during exposure blurs the captured image. Despite several decades of studies, image deconvolution to restore a blurred image still remains an issue, particularly in blind deconvolution cases in which the actual shape of the blur is unknown. Approaches based on cepstral analysis succeeded in restoring images degraded by a uniform blur caused by a camera moving straight in a single direction. In this paper, we propose to estimate, from a single blurred image, the point spread function (PSF) caused by a normal camera undergoing a 2D curved motion, and to restore the image. To extend the traditional cepstral analysis, we derive assumptions about the PSF effects in the cepstrum domain. In a first phase, we estimate several PSF candidates from the cepstrum of a blurred image and restore the image with a fast deconvolution algorithm. In a second phase, we select the best PSF candidate by evaluating the restored images. Finally, a slower but more accurate deconvolution algorithm recovers the latent image with the chosen PSF. We validate the proposed method with synthetic and real experiments.