no code implementations • 23 Feb 2024 • Sébastien Herbreteau, Charles Kervrann
Image denoising is probably the oldest and still one of the most active research topic in image processing.
no code implementations • 21 Feb 2024 • Sébastien Herbreteau, Charles Kervrann
We propose a unified view of non-local methods for single-image denoising, for which BM3D is the most popular representative, that operate by gathering noisy patches together according to their similarities in order to process them collaboratively.
1 code implementation • NeurIPS 2023 • Sébastien Herbreteau, Emmanuel Moebel, Charles Kervrann
In many information processing systems, it may be desirable to ensure that any change of the input, whether by shifting or scaling, results in a corresponding change in the system response.
1 code implementation • 1 Dec 2022 • Sébastien Herbreteau, Charles Kervrann
We introduce a parametric view of non-local two-step denoisers, for which BM3D is a major representative, where quadratic risk minimization is leveraged for unsupervised optimization.
1 code implementation • 1 Mar 2022 • Sébastien Herbreteau, Charles Kervrann
We propose a unified view of unsupervised non-local methods for image denoising that linearily combine noisy image patches.
1 code implementation • 31 Jul 2021 • Sébastien Herbreteau, Charles Kervrann
This work tackles the issue of noise removal from images, focusing on the well-known DCT image denoising algorithm.