Image raindrop removal method fused with multi-scale feature information

Aiming at the problem that the existence of raindrops makes the background features of rain images blurred and distorted, an image raindrop removal algorithm that integrates multi-scale feature information is proposed. First, an encoder-decoder neural network is built to learn image feature mapping. Considering the physical shape characteristics of raindrops, the raindrop shape is used to drive the attention module to capture the raindrop position. Then, a spatial and channel coordinated attention mechanism is introduced to strengthen image important spatial and channel feature weights. Then, a novel atrous spatial convolution pooling pyramid module is designed using atrous convolution, asymmetric convolution and pyramid structure to capture multi-scale features of images. Finally, skip connections are added between the encoding-decoding convolutional layers of the same scale, and the feature information is fed to the depth of the network to achieve the purpose of removing raindrops in the image. The experimental results show that the algorithm in this paper has a PSNR value of 30.75 and an SSIM value of 0.9257 on the public dataset Qian. The raindrops in the image can also be effectively removed on the self-made rainy day dataset.