Skip to Main content Skip to Navigation
Conference papers

Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration

Bruno Lecouat 1, 2 Jean Ponce 1 Julien Mairal 2
1 WILLOW - Models of visual object recognition and scene understanding
Inria de Paris, DI-ENS - Département d'informatique de l'École normale supérieure
2 Thoth - Apprentissage de modèles à partir de données massives
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
Abstract : Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling. We propose a novel differentiable relaxation of joint sparsity that exploits both principles and leads to a general framework for image restoration which is (1) trainable end to end, (2) fully interpretable, and (3) much more compact than competing deep learning architectures. We apply this approach to denoising, jpeg deblocking, and demosaicking, and show that, with as few as 100K parameters, its performance on several standard benchmarks is on par or better than state-of-the-art methods that may have an order of magnitude or more parameters.
Document type :
Conference papers
Complete list of metadata

Cited literature [37 references]  Display  Hide  Download

https://hal.inria.fr/hal-02414291
Contributor : Bruno Lecouat <>
Submitted on : Friday, July 24, 2020 - 11:27:26 AM
Last modification on : Thursday, July 1, 2021 - 5:58:09 PM

File

paper.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Bruno Lecouat, Jean Ponce, Julien Mairal. Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration. ECCV 2020 - European Conference on Computer Vision, Aug 2020, Glasgow / Virtual, United Kingdom. pp.238-254, ⟨10.1007/978-3-030-58542-6_15⟩. ⟨hal-02414291v3⟩

Share

Metrics

Record views

293

Files downloads

775