Depth-aware motion deblurring

Abstract

Motion deblurring from images that are captured in a scene with depth variation needs to estimate spatially-varying point spread functions (PSFs). We tackle this problemwith a stereopsis configuration, using depth information to help blur removal. We observe that the simple scheme to partition the blurred images into regions and estimate their PSFs respectively may make small-size regions lack necessary structural information to guide PSF estimation and accordingly propose region trees to hierarchically estimate them. Erroneous PSFs are rejected with a novel PSF selection scheme, based on the shock filtering invariance of natural images. Our framework also applies to general single-image spatially-varying deblurring.