LAUSR

A lot of natural images denoising methods have been proposed, however, there are dual primary challenges for medical images denoising: 1) paired datasets are scarce and 2) medical images are often three-dimensional. In this paper,we propose an U-net and graph attention network based end-to-end unsupervised deep residual generative adversarial network ( GAT-URGAN ). We add residual and attention blocks for excavating non-local detail information. In addition, for three-dimensional structure, we introduce graph attention layers to learn global structural detail information of inter-slices. To handle unpaired training data, we utilize deep network prior and introduce dual adversarial loss to constrain the generation of noise. We perform extensive experiments on Brainweb MRI datasets, which are divided into single noise images and mixed noise images, and accomplish reformative consequences compared to recent state-of-the-art denoising approaches.