37 citations · 55 across the 7 of their papers we have counts for
5 papers · 1 filter
Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising
Sébastien Herbreteau, Michael Unser
Supervised deep learning has become the method of choice for image denoising. It involves the training of neural networks on large datasets composed of pairs of noisy and clean ima…
On normalization-equivariance properties of supervised and unsupervised denoising methods: a survey
Sébastien Herbreteau, Charles Kervrann
Image denoising is probably the oldest and still one of the most active research topic in image processing. Many methodological concepts have been introduced in the past decades an…
A unified framework of non-local parametric methods for image denoising
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 accor…
Normalization-Equivariant Neural Networks with Application to Image Denoising
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…
Linear Combinations of Patches are Unreasonably Effective for Single-Image Denoising
Sébastien Herbreteau, Charles Kervrann
In the past decade, deep neural networks have revolutionized image denoising in achieving significant accuracy improvements by learning on datasets composed of noisy/clean image pa…