论文标题
ART-SS：一种适用于不良天气影响图像的半监督修复的自适应拒绝技术
ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images
论文作者
论文摘要
近年来，基于卷积神经网络的单图像不利天气去除方法已在许多基准数据集上取得了显着的性能提高。但是，这些方法需要大量的干净降解图像对进行训练，这在实践中通常很难获得。尽管文献中存在各种天气降解合成方法，但合成生成的天气降解图像的使用通常会导致由于合成图像和现实世界图像之间的域间隙而导致的真实天气降低图像的次优性能。为了解决这个问题，已经提出了各种半监督修复（SSR）方法来驱逐或飞行，这些方法学会使用合成生成的数据集来恢复干净的图像，同时使用未标记的现实世界图像更好地概括更好的概括。半监督方法的性能本质上是基于未标记数据的质量。特别是，如果未标记的数据特征与标记的数据的数据非常不同，则半监督方法的性能会大大降低。从理论上讲，我们研究了未标记的数据对SSR方法性能的影响，并开发了一种拒绝降低性能的未标记图像的技术。广泛的实验和消融研究表明，提出的样品排斥方法可显着提高现有的SSR DERANE和DYHAZED方法的性能。代码可在以下网址找到：https：//github.com/rajeevyasarla/art-ss
In recent years, convolutional neural network-based single image adverse weather removal methods have achieved significant performance improvements on many benchmark datasets. However, these methods require large amounts of clean-weather degraded image pairs for training, which is often difficult to obtain in practice. Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weather degraded images due to the domain gap between synthetic and real-world images. To deal with this problem, various semi-supervised restoration (SSR) methods have been proposed for deraining or dehazing which learn to restore the clean image using synthetically generated datasets while generalizing better using unlabeled real-world images. The performance of a semi-supervised method is essentially based on the quality of the unlabeled data. In particular, if the unlabeled data characteristics are very different from that of the labeled data, then the performance of a semi-supervised method degrades significantly. We theoretically study the effect of unlabeled data on the performance of an SSR method and develop a technique that rejects the unlabeled images that degrade the performance. Extensive experiments and ablation study show that the proposed sample rejection method increases the performance of existing SSR deraining and dehazing methods significantly. Code is available at :https://github.com/rajeevyasarla/ART-SS
