Dual attention-guided residual U-Net for image denoising

Abstract

We propose a Dual Attention-guided Residual U-Net (DARUNet) for Image denoising. This learning is based on the U-Net with several dual attention residual modules embedded in the network to realize the image denoising of Gaussian synthesis noise. Specifically, our method combines the advantages of the reconstruction and transformation of U-Net, the ability of strengthening model training of residual structure and the guiding role of attention mechanism. Among them, the attention mechanism adopts the dual attention parallel structure including spatial attention and channel attention, which effectively guides the model to reduce noise. The model can retain the detail features of the original image, and has excellent ability for image denoising. The resulting images are more natural and have higher quality. A large number of experimental results show that our method achieves advanced performance qualitatively and quantitatively.