Channel attention-based swin-transformer image denoising method and system

Abstract

The invention provides a channel attention (CA)-based Swin-Transformer image denoising method and system. A noisy image is inputted into a trained and optimized denoising network model. A shallow layer feature extraction network in the denoising network model first extracts shallow layer feature information such as noise and channels in the noisy image. The extracted shallow layer feature information is then inputted into a deep layer feature extraction network in the denoising network model to acquire deep layer feature information. Subsequently, the shallow layer feature information and the deep layer feature information are inputted into a reconstruction network of the denoising network model to perform feature fusion, so that a clear image can be obtained, thereby overcoming problems that an image denoising method based on a deep convolutional neural network is prone to a loss of details in an inputted noisy image and high computing memory and time consumption.