Abstract
The quality of images captured outdoors is often affected by the weather. One factor that interferes with sight is rain, which can obstruct the view of observers and computer vision applications that rely on those images. The work aims to recover rain images by removing rain streaks via Self-supervised Reinforcement Learning (RL) for image deraining (SRL-Derain). We locate rain streak pixels from the input rain image via dictionary learning and use pixel-wise RL agents to take multiple inpainting actions to remove rain progressively. To our knowledge, this work is the first attempt where self-supervised RL is applied to image deraining. Experimental results on several benchmark image-deraining datasets show that the proposed SRL-Derain performs favorably against state-of-the-art few-shot and self-supervised deraining and denoising methods.
Abstract (translated)
户外捕获图像的质量通常受到天气的影响。一个影响观察者视线的因素是雨，它可能会遮挡依靠这些图像进行视觉检测的应用程序。本研究旨在通过自监督强化学习（RL）去除雨条纹来恢复雨图像。我们通过字典学习从输入雨图像中定位雨条纹像素，并使用像素级的RL代理进行多次修复操作，以逐渐去除雨。据我们所知，这是第一个将自监督强化学习应用于图像去雨的尝试。在多个基准图像去雨数据集上进行的实验结果表明，与最先进的少样本和自监督去雨方法相比，所提出的SRL-Derain具有优势。
URL
https://arxiv.org/abs/2403.18270
