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 diffusion process, whose initial conditions depend on the radiance and whose diffusion 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 efficient 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 diffusion. 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.
In a separate paper, they also REF address the problem of simultaneously inferring the depth map, radiance and motion, from motion blurred and defocused.
Scene and motion reconstruction from defocused and motion-blurred images via anisotropic diffusion
In Computer Vision - ECCV 2004, 8th European Conference on Computer Vision
Computer Science