The quality of photos is highly susceptible to severe weather such as heavy rain; it can also degrade the performance of various visual tasks like object detection. Rain removal is a challenging problem because rain streaks have different appearances even in one image. Regions where rain accumulates appear foggy or misty, while rain streaks can be clearly seen in areas where rain is less heavy. We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network (MSGMFFNet). Specially, to deal with rain streaks, our method generates a rain streak attention map, while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation. Using these tools, the model can restore a result with abundant details. Furthermore, a hybrid multiscale loss combining $L_1$ loss and edge loss is used to guide the training process to pay attentionto edge and content information. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.

Deep convolutional neural networks (DCNNs)have been widely deployed in real-world scenarios. However, DCNNs are easily tricked by adversarial examples, which present challenges for critical app-lications, such as vehicle classification. To address this problem, we propose a novel end-to-end convolutional network for joint detection and removal of adversarial perturbations by denoising (DDAP). It gets rid of adversarial perturbations using the DDAP denoiser based on adversarial examples discovered by the DDAP detector. The proposed method can be regarded as a pre-processing step—it does not require modifying the structure of the vehicle classification model and hardly affects the classification results on clean images. We consider four kinds of adversarial attack (FGSM, BIM, DeepFool, PGD) to verify DDAP’s capabilities when trained on BIT-Vehicle and other public datasets. It provides better defense than other state-of-the-art defensive methods.