Images captured in challenging environments--such as nighttime, foggy, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality. Effective restoration of these degraded images is critical for the subsequent vision tasks. While many existing approaches have successfully incorporated specific priors for individual tasks, these tailored solutions limit their applicability to other degradations. In this work, we propose a universal network architecture, dubbed "ReviveDiff", which can address a wide range of degradations and bring images back to life by enhancing and restoring their quality. Our approach is inspired by the observation that, unlike degradation caused by movement or electronic issues, quality degradation under adverse conditions primarily stems from natural media (such as fog, water, and low luminance), which generally preserves the original structures of objects. To restore the quality of such images, we leveraged the latest advancements in diffusion models and developed ReviveDiff to restore image quality from both macro and micro levels across some key factors determining image quality, such as sharpness, distortion, noise level, dynamic range, and color accuracy. We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions: Rainy, Underwater, Low-light, Smoke, and Nighttime Hazy. Our experimental results demonstrate that ReviveDiff outperforms the state-of-the-art methods both quantitatively and visually.

翻译：在夜间、雾天、雨天及水下等恶劣环境下拍摄的图像通常会出现严重退化，导致视觉质量大幅下降。有效恢复这些退化图像对后续视觉任务至关重要。尽管现有许多方法已成功针对特定任务融入了专用先验知识，但这些定制化解决方案限制了其应对其他退化类型的泛化能力。本研究提出一种通用网络架构，命名为“ReviveDiff”，能够处理多种退化类型，通过增强与恢复图像质量使其重获生机。该方法的灵感源于以下观察：与运动或电子干扰引起的退化不同，恶劣环境下的质量退化主要源于自然介质（如雾气、水体、低照度），这些介质通常能保持物体的原始结构。为恢复此类图像质量，我们借助扩散模型的最新进展，开发了ReviveDiff模型，从决定图像质量的若干关键因素（包括清晰度、畸变、噪声水平、动态范围与色彩准确度）的宏观与微观层面进行质量恢复。我们在涵盖五种退化条件（雨雾、水下、低光照、烟尘、夜间朦胧）的七个基准数据集上对ReviveDiff进行了严格评估。实验结果表明，ReviveDiff在定量指标与视觉感知上均优于当前最先进的方法。