Abstract: Due to the rapid development of GPU computing,deep learning has been applied in image denoising recently.Most of the deep learning methods require noise-free images as training labels,but they are usually difficult or even impossible to obtain.Therefore,some scholars begin to study the use of noisy images for noise reduction network training,but the restored image is faced with the problem of losing details.Inspired by the idea of Noise2Noise(N2N),this paper uses pairs of noised images to train the neural network,to learn the distribution relationship between the same type of noise in the same range,and realize a new novel image denoising network model.The newly-developed model(MA-UNet) is based on the classic UNet architecture and combines the multi-head attention mechanism and simple residual network.It can capture the key information of the image,master the glo-bal information of the feature,so as to recover clearer images.Compared with the traditional algorithm CBM3D and other me-thods,such as DnCNN and B2U,MA-UNet has excellent performance in terms of parameters.Through the comparison of visual images,our model restores much clearer image details.Compared with the model designed by N2N,under different noise magnitude,the mean value of the peak signal-to-noise ratio and the structural similarity index of the proposed model on four classical data sets improve significantly.

Key words: Deep learning, Attention mechanism, Detail information, Image denoising, Global feature