用光照引导扩散模型，修复复杂夜间图像的多类退化问题
IG-Diff: Complex Night Scene Restoration with Illumination-Guided Diffusion Model
- 在扩散模型中加入光照引导模块，分步优化夜间图像恢复
- 在自建数据集上实现低光与天气退化的联合修复，保留纹理细节
- 适合需要夜间视觉增强的自动驾驶与安防系统使用
夜间环境下，人和机器感知环境极具挑战。现有图像修复方法虽能应对单一退化，但在同时存在低光照与天气等多重退化时表现不佳。由于缺乏同时包含低光与其它退化形式的成对数据，端到端解决方案难以构建。为此，本文构建了模拟光照退化与其他退化共存的复杂夜间场景数据集。为应对夜间退化的复杂性，提出在扩散模型中嵌入光照引导模块，指导光照恢复过程。该模型能在低光场景下有效应对多种退化，同时保持纹理保真度。
原文摘要 · Abstract (English)
In nighttime circumstances, it is challenging for individuals and machines to perceive their surroundings. While prevailing image restoration methods adeptly handle singular forms of degradation, they falter when confronted with intricate nocturnal scenes, such as the concurrent presence of weather and low-light conditions. Compounding this challenge, the lack of paired data that encapsulates the coexistence of low-light situations and other forms of degradation hinders the development of a comprehensive end-to-end solution. In this work, we contribute complex nighttime scene datasets that simulate both illumination degradation and other forms of deterioration. To address the complexity of night degradation, we propose an integration of an illumination-guided module embedded in the diffusion model to guide the illumination restoration process. Our model can preserve texture fidelity while contending with the adversities posed by various degradation in low-light scenarios.
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