Graph Attention in Attention Network for Image Denoising

Image denoising aims to remove the noise from noisy images. With the increasing complexity of the noise within the noisy images, current denoising methods cannot satisfactorily address this issue. This article proposes a graph attention in attention network (GAiA-Net) for image denoising. First, we introduce a novel approach to graph construction for the GAiA-Net. In the process of such graph construction, the noisy images are divided into patches to formulate the nodes in a graph. The edges are initialized using $k $ -nearest neighbors. Hence, through iterative transformation and learning, both the pixel-level and structure-level features can be captured by different information exchanges and aggregation within (pixel-level) and outside (structure-level) of the nodes, respectively. Second, we propose the graph attention in attention (GAiA) in the GAiA-Net. The proposed GAiA produces the pixel-level attention within nodes to be further induced to the nodes with various distances to generate the final attention. Therefore, our GAiA-Net can capture the long dependencies on both the pixel-level and structure-level features, which can effectively reduce the complex noise in the denoising process. Comprehensive experiments demonstrate that the proposed GAiA-Net produces state-of-the-art performances on both synthetic noise image and real noise image datasets. Especially, when experimenting on complex noisy Nam datasets, our GAiA-Net achieves a PSNR of 40.40 dB and SSIM of 0.989. These results prove the satisfactory potential and effectiveness of our GAiA-Net.