Most outdoor vision systems can be influenced by rainy weather conditions. We present a single-image rain removal method, called ResDerainNet.The proposed network can automatically detect rain streaks and remove them. Based on the deep convolutional neural networks (CNN), we learn the mapping relationship between rainy and residual images from data. Furthermore, for training, we synthesize rainy images considering various rain models. Specifically, we mainly focus on the composite models as well as orientations and scales of rain streaks. In summary, we make following contributions; - A residual deep network is introduced to remove rain noise. Unlike the plane deep network which learns the mapping relationship between noisy and clean images, we learn the relationship between rainy and residual images from data. This speeds up the training process and improves the de-raining performance.

• An automatic rain noise generator is introduced to obtain synthetic rain noise. Most de-raining methods create rain noise by using Photoshop. Since synthetic rain noise has many parameters, it is difficult to automatically adjust these parameters. In our method, we can easily change some parameters on MATLAB, which saves time and effort to get natural rain noise.

• A combination of linear additive composite model and screen blend model is proposed to make synthetic rainy images. In order for the training network to be applicable to a wide range of rainy images, only one composite model is not enough. Our experimental results show that a combination of these models achieves better performance than using either model.

Most outdoor vision systems can be influenced by rainy weather conditions. We present a single-image rain removal method, called ResDerainNet.The proposed network can automatically detect rain streaks and remove them. Based on the deep convolutional neural networks (CNN), we learn the mapping relationship between rainy and residual images from data. Furthermore, for training, we synthesize rainy images considering various rain models. Specifically, we mainly focus on the composite models as well as orientations and scales of rain streaks. In summary, we make following contributions; - A residual deep network is introduced to remove rain noise. Unlike the plane deep network which learns the mapping relationship between noisy and clean images, we learn the relationship between rainy and residual images from data. This speeds up the training process and improves the de-raining performance.

• An automatic rain noise generator is introduced to obtain synthetic rain noise. Most de-raining methods create rain noise by using Photoshop. Since synthetic rain noise has many parameters, it is difficult to automatically adjust these parameters. In our method, we can easily change some parameters on MATLAB, which saves time and effort to get natural rain noise.

• A combination of linear additive composite model and screen blend model is proposed to make synthetic rainy images. In order for the training network to be applicable to a wide range of rainy images, only one composite model is not enough. Our experimental results show that a combination of these models achieves better performance than using either model.