Multi-Shot Deblurring for 3D Scenes

The presence of motion blur is unavoidable in hand-held cameras, especially in low-light conditions. In this paper, we address the inverse rendering problem of estimating the latent image, scene depth and camera motion from a set of differently blurred images of the scene. Our framework can account for depth variations, non-uniform motion blur as well as mis-alignments in the captured observations. We initially describe an iterative algorithm to estimate ego motion in 3D scenes by suitably harnessing the point spread functions across the blurred images at different spatial locations. This is followed by recovery of latent image and scene depth by alternate minimization.