Abstract

We propose a solution to the problem of inferring the depth map, radiance and motion of a scene from a collection of motion- blurred and defocused images. We model motion-blur and defocus as an anisotropic difiusion process, whose initial conditions depend on the radiance and whose difiusion tensor encodes the shape of the scene, the motion field and the optics parameters. We show that this model is well- posed and propose an eficient algorithm to infer the unknowns of the model. Inference is performed by minimizing the discrepancy between the measured blurred images and the ones synthesized via forward difiusion. Since the problem is ill-posed, we also introduce additional Tikhonov regularization terms. The resulting method is fast and robust to noise as shown by experiments with both synthetic and real data.