Abstract
Images taken through window glass are often degraded by contaminants adhered to the glass surfaces. Such contaminants cause occlusions that attenuate the incoming light and scatter stray light towards the camera. Most of existing deep learning methods for neutralizing the effects of contaminated glasses relied on synthetic training data. Few researchers used real degraded and clean image pairs, but they only considered removing or alleviating the effects of rain drops on glasses. This paper is concerned with the more challenging task of learning the restoration of images taken through glasses contaminated by a wide range of occluders, including muddy water, dirt and other small foreign particles found in reality. To facilitate the learning task we have gone to a great length to acquire real paired images with and without glass contaminants. More importantly, we propose an all-in-one model to neutralize contaminants of different types by utilizing the one-shot test-time adaptation mechanism. It involves a self-supervised auxiliary learning task to update the trained model for the unique occlusion type of each test image. Experimental results show that the proposed method outperforms the state-of-the-art methods quantitatively and qualitatively in cleaning realistic contaminated images, especially the unseen ones.
Abstract (translated)
通过窗户玻璃拍摄的照片常常因粘附在玻璃表面的污染物而质量下降。这些污染物会导致光衰减并散射杂散光线进入相机，从而造成图像模糊和清晰度降低。现有的大多数深度学习方法依赖于合成训练数据来消除受污染玻璃的影响。尽管有一些研究人员使用了真实受损与清洁成对的图像，但他们仅关注去除或减轻雨水滴在玻璃上的影响。本文则更深入地探讨了一个更具挑战性的任务：通过学习如何恢复被各种遮挡物（包括泥水、灰尘和其他现实中的微小外来颗粒）污染的玻璃所拍摄到的图像。 为了便于这个研究任务，我们费尽心思获取了带有和不带玻璃污染物的真实成对照片。更重要的是，我们提出了一种集大成的一体化模型，通过利用一次性测试时适应机制来中和不同类型的不同遮挡物的影响。该方法包括了一个自监督辅助学习任务，用于根据每个测试图像的独特遮挡类型更新训练好的模型。 实验结果表明，所提出的这种方法在清洁实际受污染的图像方面优于现有的最先进方法，在定量和定性评估上都取得了显著效果，特别是在处理未见过的新鲜样本时表现更优。
URL
https://arxiv.org/abs/2509.01033
