Scene and Motion Reconstruction from Defocused and Motion-Blurred Images via Anisotropic Diffusion
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.
Cite
Text
Favaro et al. "Scene and Motion Reconstruction from Defocused and Motion-Blurred Images via Anisotropic Diffusion." European Conference on Computer Vision, 2004. doi:10.1007/978-3-540-24670-1_20Markdown
[Favaro et al. "Scene and Motion Reconstruction from Defocused and Motion-Blurred Images via Anisotropic Diffusion." European Conference on Computer Vision, 2004.](https://mlanthology.org/eccv/2004/favaro2004eccv-scene/) doi:10.1007/978-3-540-24670-1_20BibTeX
@inproceedings{favaro2004eccv-scene,
title = {{Scene and Motion Reconstruction from Defocused and Motion-Blurred Images via Anisotropic Diffusion}},
author = {Favaro, Paolo and Burger, Martin and Soatto, Stefano},
booktitle = {European Conference on Computer Vision},
year = {2004},
pages = {257-269},
doi = {10.1007/978-3-540-24670-1_20},
url = {https://mlanthology.org/eccv/2004/favaro2004eccv-scene/}
}