Endoscopic Image Denoising Algorithm Based on Spatial Attention UNet

Endoscopic image has complex backgrounds and spatially different noise, bringing mainstream denoising methods to the problem of incomplete noise removal and the loss of image detail. Thus, an endoscopic image denoising algorithm based on spatial attention UNet network is proposed in this paper. UNet based on residual learning is utilized as the backbone network. Spatial attention modules based on noise intensity estimation and edge feature extraction modules are used to remove noise better while preserving the image details and improving generalization ability. We take endoscopic images of real scenes using gastroscopy and compare our method with mainstream methods. Experimental results show that our approach improves PSNR by 3.51 or 2.93 and SSIM by 0.03 or 0.015 compared with CBDNet or EDCNN, respectively. Our method can effectively improve the impact of noise on the image quality of endoscopic images, thus better assisting doctors in diagnosis and treatment.