A Novel Data and Model Hybrid-Driven Method for Image Restoration Using Residual Dense Attention U-Net

As people's pursuit of large screen-to-body ratio screen experience continues to improve, neither the digging front camera nor the bangs front camera can meet people's requirements for the front camera of a mobile phone. Therefore, the research and development of full-screen equipment has become a new trend. A full-screen device requires the imaging device to be placed below the screen, which we call an under-display cameras. The under-display cameras will improve the user's interactive experience while expanding the screen-to-body ratio of the mobile phone. However, there are many problems in the development of under-display cameras. When the imaging device is installed under the screen, the lower light transmittance will cause serious image degradation. Therefore, a new U-Net, which we call residual dense attention UNet (RDAU-Net), is proposed in this paper. A residual dense attention module which we propose in RDAU-Net to replace the single-layer convolution in the U-Net network. Meanwhile, the introduction of channel attention can effectively enhance the interdependence between channels, thereby adaptively re-dividing channel features. Experiments show that our RDAU-Net has better accuracy and faster recovery efficiency than existing methods.