4D Anisotropic Diffusion Framework with PDEs for Light Field Regularization and Inverse Problems

Pierre Allain (1) , Laurent Guillo (1) , Christine Guillemot (1)

1 Sirocco - Analysis representation, compression and communication of visual data

In this paper, we consider the problem of vector-valued regularization of light fields based on PDEs. We propose a regularization method operating in the 4D ray space that does not require prior estimation of disparity maps. The method performs a PDE-based anisotropic diffusion along directions defined by local structures in the 4D ray space. We analyze light field regularization in the 4D ray space using the proposed 4D anisotropic diffusion framework by first considering a light field toy example, i.e., a tesseract. This simple light field example allows an in-depth analysis of how each eigenvector influences the diffusion process. We then illustrate the diffusion effect for several light field processing applications: denoising, angular and spatial interpolation, regularization for enhancing disparity estimation as well as inpainting.

Mots clés: anisotropic diffusion; inverse problems; Index Terms-Light Field; regularization

Domaines: Traitement du signal et de l'image [eess.SP]

Pierre Allain, Laurent Guillo, Christine Guillemot. 4D Anisotropic Diffusion Framework with PDEs for Light Field Regularization and Inverse Problems. IEEE Transactions on Computational Imaging, 2019, pp.1-16. ⟨10.1109/TCI.2019.2919229⟩. ⟨hal-02135541⟩