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 perceptua
GLOW-FM is a one-step latent flow-matching framework that enables efficient generative low-light image enhancement via single-function evaluation in a compact latent space.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
GLOW-FM: Efficient Generative Low-Light Image Enhancement with One-Step Latent Flow Matching was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 11 checks on 2 different days. Its best position was #19 of 30, reached on Nov 3, 03:28 UTC. It dropped off the list after Nov 3, 09:28 UTC.
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#19 on Hugging Face · Daily papers, on Nov 3, 03:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 2, 2026 and has been on the list on 2 days (11 checks) since.