Radar-SR3: A Weather Radar Image Super-Resolution Generation Model Based on SR3

Abstract

To solve the problems of the current deep learning radar extrapolation model consuming many resources and the final prediction result lacking details, a weather radar image super-resolution weather model based on SR3 (super-resolution via image restoration and recognition) for radar images is proposed. This model uses a diffusion model to super-resolve weather radar images to generate high-definition images and optimizes the performance of the U-Net denoising network on the basis of SR3 to further improve image quality. The model receives high-resolution images with Gaussian noise added and performs channel splicing with low-resolution images for conditional generation. The experimental results showed that the introduction of the diffusion model significantly improved the spatial resolution of weather radar images, providing new technical means for applications in related fields; when the amplification factor was 8, Radar-SR3, compared with the image super-resolution model based on the generative adversarial network (SRGAN) and the bicubic interpolation algorithm, the peak signal-to-noise ratio (PSNR) increased by 146% and 52% on average. According to this model, it is possible to train radar extrapolation models with limited computing resources with high-resolution images.

Keywords

• weather radar image
• super-resolution
• diffusion model
• attention mechanism
• residual connection
• S-band radar