Ariel Lapid, Yuval Haitman, Reuven Peretz, Arnon Netzer, Idit Diamant · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31222 · open-access PDF
낮은 빛 이미지 향상 (LLIE)은 심하게 악화된 관찰에서 잘 노출되고 감각적으로 충실한 이미지를 복구하는 것을 목표로합니다. 최근의 분산 기반 생성 방법은 강력한 이미지를 사용함으로써 강력한 감각적 품질을 달성하지만, 반복적 샘플링은 실용적인 배포를 제한하여 상당한 계산 비용을 부담합니다.
Machine translation (Meta M2M100); the English original follows.
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 l
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