###### Abstract  
Images acquired using vision sensors are easily affected by environmental limitations, especially rain streaks. These streaks will seriously reduce image quality, which, in turn, reduces the accuracy of the algorithms that use the resulting images in vision sensor systems. In this paper, we proposed a method that combined the bilateral filter with the generative adversarial network to eliminate the interference of rain streaks. Unlike other methods that use all the information in an image as the input to the generative adversarial network, we used a bilateral filter to preprocess and separate the high frequency part of the original image. The generator for the high-frequency layer of the image was designed to generate an image with no rain streaks. The high-frequency information of the image was used in a high-frequency global discriminator designed to measure the authenticity of the generated image from multiple perspectives. We also designed a loss function based on the structural similarity index to further improve the effect of removal of the rain streaks. An ablation experiment proved the validity of the method. We also compared images in synthetic and real-world datasets. Our method could retain more image information, and the generated image was clearer. vision sensors; rain streaks removal; bilateral filter; generative adversarial network +
Footnote †: journal: Applied  
**Accepted: 22 May 2023**  
**Multi-dimensional** (2023) 2023 2  
been applied to rain streaks removal by directly learning the nonlinear mapping between input and output to improve the quality of the image [10]. Traditional single-frame image-based methods to remove rain streaks are generally dependent on a model of rainy images that is challenging to build. It is also difficult to separate the clear background image from the rain streaks in the image, which prevents the complete removal of the streaks. The relevant methods generally also have high algorithmic complexity. A large number of features are duplicated or redundant between the rainy image and the image without streaks. Most single-frame image-based methods of removing rain streaks that use deep learning employ direct mapping to learn the mapping relationships, which enhances the difficulty and complexity of network learning [11; 12]. The currently used loss function is also not strongly constrained, which may lead to image blurring or the incomplete removal of the rain streaks. Therefore, we propose a method to improve the image quality [13]. It involves first using a bilateral filter to separate the high-frequency part of the rainy image to eliminate the interference from its low-frequency part. A generator is then used to process only the high-frequency part, thereby simplifying the input information. The loss of structural similarity index (SSIM) is proposed as an additional constraint to enhance the effect of removing rain streaks. Finally, the high-frequency global (H-G) discriminator is used to comprehensively evaluate the high-frequency and global parts of the generated image. The main contributions of this paper are as follows:  
* We used a bilateral filter to separate the high-frequency part of the original image. * An image with no rain streaks, generated by the high-frequency layer of the image. We measured the authenticity of the generated image by a high-frequency global discriminator. The generator for the high-frequency layer of the image was designed to generate an image without rain streaks. We designed a high-frequency global discriminator to measure the authenticity of the generated image from multiple perspectives. * We proposed the novel loss function based on the structural similarity index to further improve the effect of the rain streaks. Section 2 compares related methods for removing rain streaks based on single-frame images, consisting of image decomposition and deep learning. Section 3 proposes a novel method that combines the bilateral filter and the GAN and proposes a network.