3 papers
eess.IV2026
Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring
Jérémy Scanvic, Mike Davies, Patrice Abry +1
Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scien…
stat.ML2026
Self-Supervised Learning from Noisy and Incomplete Data
Julián Tachella, Mike Davies
Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known. Historically, this…
stat.ML2025
UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate
Julián Tachella, Mike Davies, Laurent Jacques
Recently, many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone, bypassing the need for ground-truth references. Mo…