一 shot 适应真实玻璃污渍，无需合成数据即可清除多种遮挡。
Seeing through Unclear Glass: Occlusion Removal with One Shot
- 用真实成对图像训练，通过自监督机制实现测试时一次性适应
- 在真实污渍图像上优于现有方法，尤其对未见过的污渍类型
- 适合户外视觉、安防监控等需要实时去污的场景
通过窗户拍摄的图像常因玻璃表面附着物导致退化，这些污染物会遮挡光线并散射杂光。现有深度学习方法多依赖合成数据，少数使用真实成对图像的研究仅针对雨滴等单一污渍。本文关注更复杂的任务：恢复被多种现实污染物（如泥水、灰尘、微小异物）遮挡的图像。为此，我们精心采集了大量真实有/无污渍配对图像。更重要的是，提出一种全功能模型，利用一 shot 测试时自适应机制，通过自监督辅助任务更新模型以适配每张测试图的独特污渍类型。实验表明，该方法在定量和定性指标上均优于当前最优方法，尤其在处理未见过的污渍时表现突出。
原文摘要 · Abstract (English)
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.
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