# ControlLight：面向可控、一致且可泛化的低光增强

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-25 08:00
- AIHOT 分数：55
- AIHOT 链接：https://aihot.virxact.com/items/cmpm4ktyn0liesl011ec4iel4
- 原文链接：https://arxiv.org/abs/2605.25569

## AI 摘要

现有基于深度学习的低光增强方法受限于有限数据集和单一增强目标，泛化能力和可控性不足。本文提出ControlLight框架，通过构建带有连续光照强度监督的大规模真实退化图像数据集，并引入感知对齐加权流匹配损失以确保不同控制强度下输出结构的一致性，实现了对增强强度的灵活控制。实验表明，该方法在低光增强任务上达到了最先进的性能，同时具备强连续可控性和对真实场景的良好泛化能力。

## 正文

Existing deep learning-based low-light enhancement methods are typically trained on limited datasets with single enhancement targets, which restricts their generalization ability and controllability in real-world applications. To overcome these limitations, we propose ControlLight, a controllable, consistent, and generalizable framework for low-light enhancement. We first construct a large-scale dataset of real-world degraded images with continuous illumination-strength supervision. To further ensure consistent outputs under different control strengths, we introduce a misalignment-aware weighted flow matching loss that preserves image structure across continuous enhancement strengths. ControlLight allows users to edit real-world degraded low-light images toward satisfactory enhancement results by flexibly controlling the strength while preserving visual consistency and realism. Extensive experiments show that ControlLight achieves state-of-the-art performance against existing low-light enhancement approaches while demonstrating strong continuous controllability and generalization to real-world scenarios.