RA-UNet: an improved network model for image denoising.

Due to the rapid advancement of GPU computing, deep learning has lately been widely used in image denoising. Most deep learning methods require noise-free images as labels, which are often difficult or impossible to obtain. Therefore, denoising network models have to be trained with a pair of noisy and low-noise images. However, the restored images still face the problem of losing detail information. In this paper, we propose a novel denoising network model based on the concept of Noise2Noise (N2N), where pairs of noisy images are utilized to train a neural network that can learn the noise distribution relationship between them. This newly-proposed model (RA-UNet) draws inspiration from the classical UNet architecture and is designed with a multi-Residual convolutional block with Attention that can adapt different scales to mine the key information of images and recover clearer images. The denoising performance of RA-UNet is comparable and better than that of the conventional CBM3D, while the proposed RA-UNet performs significantly better on both PSNR and SSIM with less 2.5 % FLOPs and less 3 % running time compared to existing deep learning-based methods, such as DnCNN and ADNet. From the perspective of visual quality, RA-UNet can restore images with higher clarity. Compared with the UNet model, our model improves the average PSNR and SSIM obtained by testing Gaussian noise ( σ = 50 ) images on four classic datasets by 2.28 dB and 0.0552, respectively. [ABSTRACT FROM AUTHOR]