arXiv Computer Vision October 23, 2026 By Ariel Lapid, Yuval Haitman, Reuven Peretz, Arnon Netzer, Idit Diamant

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

Read the original on arXiv Computer Vision →

GLOW-FM is a one‑step latent flow‑matching framework designed for efficient generative low‑light image enhancement (LLIE). It operates in the latent space of a frozen Tiny‑VAE, using Terminal Velocity Matching (TVM) to achieve high‑quality enhancement with a single function evaluation. Experiments on real‑world benchmarks show competitive image quality, strong cross‑dataset generalization, and low computational cost (34 GFLOPs for the small variant).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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