Abstract
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.
Abstract (translated)
拍摄于具有挑战性的环境中的图像通常会遭受显著的失真，导致视觉质量的大幅降低。有效恢复这些失真的图像对于后续视觉任务至关重要。虽然许多现有方法已经成功地引入了特定任务的先验，但这些自适应解决方案将它们的适用性限制在其他的失真上。在这项工作中，我们提出了一个名为“ReviveDiff”的通用网络架构，它能够解决广泛的失真并通过增强和恢复图像的质量使图像焕发新生。我们的方法源于一个观察，即与运动或电子问题引起的失真不同，不良条件下导致的质量失真主要源于自然媒体（如雾、水、低亮度），这些媒体通常保留对象的原始结构。为了恢复这类图像的质量，我们利用扩散模型的最新进展，开发了ReviveDiff，从宏观和微观水平恢复图像质量，涵盖决定图像质量的一些关键因素，如清晰度、变形、噪声水平、动态范围和色彩准确性。我们对ReviveDiff在七种基准数据集上的实验结果进行了评估，涵盖了五种不同的失真类型：雨天、水下、低光、烟雾和夜间阴霾。我们的实验结果表明，ReviveDiff在量化和视觉上均优于最先进的 methods。
URL
https://arxiv.org/abs/2409.18932
