1 citations · 2 across the 4 of their papers we have counts for
4 papers
Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging
Victor Sechaud, Laurent Jacques, Patrice Abry +1
Learning based methods are now ubiquitous for solving inverse problems, but their deployment in real-world applications is often hindered by the lack of ground truth references for…
Self-supervised learning for phase retrieval
Victor Sechaud, Patrice Abry, Laurent Jacques +1
In recent years, deep neural networks have emerged as a solution for inverse imaging problems. These networks are generally trained using pairs of images: one degraded and the othe…
Equivariance-based self-supervised learning for audio signal recovery from clipped measurements
Victor Sechaud, Laurent Jacques, Patrice Abry +1
In numerous inverse problems, state-of-the-art solving strategies involve training neural networks from ground truth and associated measurement datasets that, however, may be expen…
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…