1 citations · 1 across the 11 of their papers we have counts for
15 papers
Seeing Through Smoke: Surgical Desmoking for Improved Visual Perception
Jingpei Lu, Fengyi Jiang, Xiaorui Zhang +2
Minimally invasive and robot-assisted surgery relies heavily on endoscopic imaging, yet surgical smoke produced by electrocautery and vessel-sealing instruments can severely degrad…
UNet-AF: An alias-free UNet for image restoration
Jérémy Scanvic, Quentin Barthélemy, Julián Tachella
The simplicity and effectiveness of the UNet architecture makes it ubiquitous in image restoration, image segmentation, and diffusion models. They are often assumed to be equivaria…
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 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…
Efficient Unrolled Networks for Large-Scale 3D Inverse Problems
Romain Vo, Julián Tachella
Deep learning-based methods have revolutionized the field of imaging inverse problems, yielding state-of-the-art performance across various imaging domains. The best performing net…
Equivariant Deep Equilibrium Models for Imaging Inverse Problems
Alexander Mehta, Ruangrawee Kitichotkul, Vivek K Goyal +1
Equivariant imaging (EI) enables training signal reconstruction models without requiring ground truth data by leveraging signal symmetries. Deep equilibrium models (DEQs) are a pow…