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Under-Display Camera (UDC) has been widely exploited to help smartphonesrealize full screen display. However, as the screen could inevitably affect thelight propagation process, the images captured by the UDC system usuallycontain flare, haze, blur, and noise. Particularly, flare and blur in UDCimages could severely deteriorate the user experience in high dynamic range(HDR) scenes. In this paper, we propose a new deep model, namely UDC-UNet, toaddress 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 UDCsystem is known, we treat UDC image restoration as a non-blind imagerestoration problem and propose a novel learning-based approach. Our networkconsists of three parts, including a U-shape base network to utilizemulti-scale information, a condition branch to perform spatially variantmodulation, and a kernel branch to provide the prior knowledge of the givenPSF. According to the characteristics of HDR data, we additionally design atone mapping loss to stabilize network optimization and achieve better visualquality. Experimental results show that the proposed UDC-UNet outperforms thestate-of-the-art methods in quantitative and qualitative comparisons. Ourapproach won the second place in the UDC image restoration track of MIPIchallenge. Codes will be publicly available.

UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network