Image denoising via multi-scale spatial frequency domain convolutional neural network Noise-Net

The existence of noise will seriously affect quality of image. Image denoising method is important for obtaining high-quality image. Most of traditional denoising methods need to estimate the noise level, which are unstable in the actual denoising scene. As a data-driven method, the development of deep learning shows great potential in the field of image denoising. In this paper, a method combining image denoising model and deep learning framework is proposed. Well-designed multi-scale restoration network Noise-Net embedded this method optimizing neural network training to obtain ideal image recovery results. By down-sampling the original noisy image input at different scales, the noisy image features are extracted. These multi-scale features are summed and combined. The addition of the residual module improves the network training ability and effectively prevents the network from overfitting. The network is optimized by Convolutional Block Attention Module (CBAM). It can enable effective extraction of image features in the spatial and frequency domains. Network input is noisy image, clear image as label. The training phase is divided into two stages: noisy data generation and simulated images for pre-training. 2000 images of DOTA 1.0 dataset constitute as training set and 1000 images as test set. By adding different noises such as Gaussian noise and Poisson noise to the image, the data set is constructed with the label image. The loss function of the absolute minimum error is calculated and sent to the Adam optimizer for parameter optimization. Numerical simulation and experimental results show that Noise-Net has an effect on image denoising ability.