Sparse representations and bayesian image inpainting

Jalal M. Fadili (1) , Jean-Luc Starck (2)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 AIM (UMR7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation

Representing the image to be inpainted in an appropriate sparse dictionary, and combining elements from bayesian statistics, we introduce an expectation-maximization (EM) algorithm for image inpainting. From a statistical point of view, the inpainting can be viewed as an estimation problem with missing data. Towards this goal, we propose the idea of using the EM mechanism in a bayesian framework, where a sparsity promoting prior penalty is imposed on the reconstructed coefficients. The EM framework gives a principled way to establish formally the idea that missing samples can be recovered based on sparse representations. We first introduce an easy and efficient sparse-representation-based iterative algorithm for image inpainting. Additionally, we derive its theoretical convergence properties for a wide class of penalties. Particularly, we establish that it converges in a strong sense, and give sufficient conditions for convergence to a local or a global minimum. Compared to its competitors, this algorithms allows a high degree of flexibility to recover different structural components in the image (piece-wise smooth, curvilinear, texture, etc). We also describe some ideas to automatically find the regularization parameter.

Domaines: Traitement des images [eess.IV]

Jalal M. Fadili, Jean-Luc Starck. Sparse representations and bayesian image inpainting. Proceedings of International Conferences SPARS'05, Nov 2005, Rennes, France. 4 pp. ⟨hal-00013840⟩