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

GLOW-FM: 用于高效低光图像增强的单步潜在流匹配

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了GLOW-FM，一个使用单步潜在流匹配方法进行高效低光图像增强的新框架。该方法在Tiny-VAE的潜在空间内运行，并采用终端速度匹配（TVM）以单次函数评估实现高质量结果。实验表明，GLOW-FM提供了具有竞争力的图像质量和卓越的跨数据集泛化能力，其较小版本仅需一步推理和34 GFLOPs，使得生成式低光图像增强更加实用。 AI

影响 引入了一种更高效的低光图像增强生成方法，可能实现更广泛的实际应用。

排序理由 详细介绍图像增强新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

GLOW-FM: 用于高效低光图像增强的单步潜在流匹配

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详细介绍图像增强新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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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