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

Japanese

最近の拡散ベースの生成方法は、強力な画像プレーヤーを活用することで強力な感覚品質を達成しますが、そのイテラティブサンプルは、実用的な生成 LLIE の実用的な展開を制限する実質的な計算コストを発生させます。 生成 LLIE の最近の効率性の向上にもかかわらず、単一機能公共評価と低総合計算による高品質の向上は、特に多様な現実世界の低光条件下で挑戦的です。 我々は、効率的な生成 LLIE のための 1 ステップの遅延フローマッチングフレームワークである GLOW-FM を導入します。 GLOW-FM は、凍結された Tiny-EVA の潜在空間で条件付き輸送を実行し、LLM

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.

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

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