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Scene and Motion Reconstruction from Defocused and Motion-Blurred Images via Anisotropic Diffusion

  • Paolo Favaro
  • Martin Burger
  • Stefano Soatto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)

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.

Keywords

Image Plane Point Spread Function Blind Deconvolution Camera Shutter Defocused Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Paolo Favaro
    • 1
  • Martin Burger
    • 2
  • Stefano Soatto
    • 1
  1. 1.Computer Science DepartmentUCLALos AngelesUSA
  2. 2.Mathematics DepartmentUCLALos AngelesUSA

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