Multi-image blind deconvolution using low-rank representation

Abstract

Blind deconvolution is a restoration process of an image which is blurred by an unknown point spread function (PSF) (a.k.a. blur kernel). Some previous works show that multiple blurred captures of the same scene can improve the quality of blind deconvolution result. However, the previous multi-image blind deconvolution methods are prone to inconsistencies between observations such as moving objects, illumination changes or mis-alignment. In this paper, we present a new multi-image blind deconvolution algorithm which natually combines low-rank approximation, moving object detection and incremental alignment with blind deconvolution in a unified framework. Our framework alternatively solves a Schatten-0 norm low-rank approximation and MAP-based L0 norm blind deconvolution for finding the true latent images and their corresponding PSFs and transformation parameters. The experimental results show that our approach can recover high quality images in the presence of possible corruptions on both static and moving scenes and outperforms the state of the art results.