  Under-Display Camera (UDC) has been widely exploited to help smartphones
realize full screen display. However, as the screen could inevitably affect the
light propagation process, the images captured by the UDC system usually
contain flare, haze, blur, and noise. Particularly, flare and blur in UDC
images could severely deteriorate the user experience in high dynamic range
(HDR) scenes. In this paper, we propose a new deep model, namely UDC-UNet, to
address the UDC image restoration problem with the known Point Spread Function
(PSF) in HDR scenes. On the premise that Point Spread Function (PSF) of the UDC
system is known, we treat UDC image restoration as a non-blind image
restoration problem and propose a novel learning-based approach. Our network
consists of three parts, including a U-shape base network to utilize
multi-scale information, a condition branch to perform spatially variant
modulation, and a kernel branch to provide the prior knowledge of the given
PSF. According to the characteristics of HDR data, we additionally design a
tone mapping loss to stabilize network optimization and achieve better visual
quality. Experimental results show that the proposed UDC-UNet outperforms the
state-of-the-art methods in quantitative and qualitative comparisons. Our
approach won the second place in the UDC image restoration track of MIPI
challenge. Codes will be publicly available.
