Newest papers

GLOW-FM: Efficient Generative Low-Light Image Enhancement with One-Step Latent Flow Matching

Ariel Lapid, Yuval Haitman, Reuven Peretz, Arnon Netzer, Idit Diamant · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31222 · cited by 0 works (OpenAlex) · open-access PDF

German

Die jüngsten diffusionsbasierten generativen Methoden erreichen eine starke Wahrnehmungsqualität, indem sie leistungsstarke Bildpräferenzen nutzen, aber ihre iterative Probenahme erleidet erhebliche berechnende Kosten und begrenzt die praktische Bereitstellung. Trotz der jüngsten Effizienzgewinne in der generativen LLIE ist eine qualitativ hochwertige Verbesserung mit einer einheitlichen öffentlichen Funktionsbewertung und einer niedrigen Gesamtrechnung nach wie vor herausfordernd, insbesondere unter vielfältigen realen niedrigen Lichtbedingungen. Wir führen GLOW-FM ein, ein einstufiges latentes Flussmatching-Framework für effiziente generative LLIE. Trotz der jüngsten Effizienzgewinne in der generativen LLIE führt GLOW-FM einen beding

Machine translation (Meta M2M100); the English original follows.

Abstract

Low-light image enhancement (LLIE) aims to recover well-exposed and perceptually faithful images from severely degraded observations. Recent diffusion-based generative methods achieve strong perceptual quality by leveraging powerful image priors, but their iterative sampling incurs substantial computational cost, limiting practical deployment. Despite recent efficiency gains in generative LLIE, high-quality enhancement with a single function evaluation and low overall computation remains challenging, particularly under diverse real-world low-light conditions. We introduce GLOW-FM, a one-step latent flow-matching framework for efficient generative LLIE. GLOW-FM performs conditional transport in the latent space of a frozen Tiny-VAE and leverages Terminal Velocity Matching (TVM) to enable enhancement with a single function evaluation. Matched comparisons with alternative one-step objectives support TVM as an effective design choice, and, to the best of our knowledge, this is its first adaptation to image restoration and specifically to LLIE. Extensive experiments on challenging real-world benchmarks demonstrate that GLOW-FM achieves competitive image quality and state-of-the-art cross-dataset generalization while requiring only a single function evaluation and 34 GFLOPs for the small variant. These findings highlight the potential of GLOW-FM to bring generative LLIE closer to practical deployment through single-step inference and compact latent modeling. Code will be made publicly available upon publication.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Inference, Artificial intelligence, Matching (statistics), Generative model, Image (mathematics)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange