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

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GLOW-FM, a novel framework for efficient low-light image enhancement using a one-step latent flow matching approach. This method operates within the latent space of a Tiny-VAE and employs Terminal Velocity Matching (TVM) to achieve high-quality results with a single function evaluation. Experiments show GLOW-FM offers competitive image quality and superior cross-dataset generalization, requiring only a single inference step and 34 GFLOPs for its smaller variant, making generative low-light image enhancement more practical. AI

IMPACT Introduces a more efficient generative approach for low-light image enhancement, potentially enabling wider practical applications.

RANK_REASON Research paper detailing a new method for image enhancement. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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Research paper detailing a new method for image enhancement. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ariel Lapid, Yuval Haitman, Reuven Peretz, Arnon Netzer, Idit Diamant ·

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

    arXiv:2610.31222v1 Announce Type: new 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…

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