Mohamad Jouni and Brayan Monroy at the GdR IASIS Workshop
15 October 2026
Mohamad Jouni and Brayan Monroy will present their work at the GdR IASIS workshop on self-supervised visual learning on 15 October 2026 at Université Paris Cité, Paris, France.
The workshop, Self-Supervised Visual Learning: Methods, Applications and Challenges, brings together researchers to discuss visual representation learning without labels, including its applications, limits in generalization, and data requirements.
Talks
Brayan Monroy
Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising
Scheduled presentation: 11:55-12:20.
Abstract
Self-supervised image denoising methods have traditionally relied on architectural constraints, pseudo-pair constructions, or specialized loss functions to avoid the trivial identity mapping. Among these, approaches such as Noisier2Noise or R2R create training pairs by adding synthetic noise to noisy images. While effective, these recorruption-based approaches require precise knowledge of the noise distribution, which is often unavailable. We present Learning to Recorrupt (L2R), a self-supervised framework that does not require exact knowledge of the noise distribution. Our method introduces a learnable recorruptor jointly optimized with the denoiser through a min-max saddle-point objective. The proposed method achieves state-of-the-art performance among methods without prior knowledge of the noise distribution across unconventional and heavy-tailed noise distributions, such as log-gamma and Laplace, as well as spatially correlated noise, while obtaining competitive results on real-world denoising benchmarks characterized by signal-dependent noise.
Mohamad Jouni
SHARP: Self-Supervised Hyperspectral Alignment and Plug-and-Play Reconstruction for RGB-Guided Super-Resolution
Scheduled presentation: 15:45-16:10.
Abstract
RGB-guided hyperspectral super-resolution (HSR) aims to recover a high-spatial-resolution hyperspectral image (HSI) by combining the spectral information of a low-resolution HSI with the spatial detail of a high-resolution RGB observation. In real dual-camera systems, however, the two modalities are rarely pixel-wise aligned. Separate viewpoints and optics induce depth-dependent parallax, occlusions, and spatially varying misregistration, while spectral, radiometric, and resolution differences make cross-modal registration particularly challenging. Consequently, inaccurate alignment may transfer inconsistent or spurious RGB structures into the reconstructed HSI. This problem is further challenged in real snapshot acquisitions by hyperspectral sensor noise and the absence of clean high-resolution hyperspectral targets. We introduce SHARP, a self-supervised framework that couples learned cross-modal alignment with Plug-and-Play hyperspectral reconstruction for real RGB-HSI acquisitions. Our approach combines cross-modal dense registration with iterative Plug-and-Play reconstruction and adapts the registration component without clean supervision. Rather than relying on clean high-resolution hyperspectral targets, the framework exploits two repeated noisy HSI measurements of the same static scene while sharing the same RGB guidance, inspired on Noise2Noise objective. We also investigate a further-recorruption strategy to generate two conditionally independent hyperspectral observations from a single noisy measurement, enabling self-supervised reconstruction when repeated acquisitions are unavailable. Experiments on real data acquired with our snapshot RGB-HSI dual-camera system, show that self-supervised adaptation can improve denoising and stabilize cross-modal registration without clean references. Our ongoing work focuses on integrating an explicit HSR forward model with self-supervised registration and reconstruction, and on studying robustness to noise, radiometric mismatch, and varying spectral configurations, beyond the commonly used 31-band [400-700] nm spectral coverage.
Workshop Details
Date: 15 October 2026
Time: 09:30-17:30 (Paris local time)
Location: LIPADE, Université Paris Cité, 45 rue des Saints-Pères, 75006 Paris, France
Visit the official workshop website for the programme and practical information.