By hiding the front-facing camera below the display panel, Under-Display Camera (UDC) provides users with a full-screen experience. However, due to the characteristics of the display, images taken by UDC suffer from significant quality degradation. Methods have been proposed to tackle UDC image restoration and advances have been achieved. There are still no specialized methods and datasets for restoring UDC face images, which may be the most common problem in the UDC scene. To this end, considering color filtering, brightness attenuation, and diffraction in the imaging process of UDC, we propose a two-stage network UDC Degradation Model Network named UDC-DMNet to synthesize UDC images by modeling the processes of UDC imaging. Then we use UDC-DMNet and high-quality face images from FFHQ and CelebA-Test to create UDC face training datasets FFHQ-P/T and testing datasets CelebA-Test-P/T for UDC face restoration. We propose a novel dictionary-guided transformer network named DGFormer. Introducing the facial component dictionary and the characteristics of the UDC image in the restoration makes DGFormer capable of addressing blind face restoration in UDC scenarios. Experiments show that our DGFormer and UDC-DMNet achieve state-of-the-art performance.

翻译：通过将前置摄像头隐藏在显示面板下方，屏下摄像头（UDC）为用户提供了全屏体验。然而，由于显示器的特性，UDC拍摄的图像存在显著的质量退化。现有方法已提出解决UDC图像修复问题并取得进展，但目前尚无专门针对UDC人脸图像修复的方法和数据集，而这可能是UDC场景中最常见的问题。为此，我们考虑UDC成像过程中的颜色滤波、亮度衰减和衍射效应，提出了一种名为UDC-DMNet的两阶段网络，通过建模UDC成像过程来合成UDC图像。随后，我们利用UDC-DMNet以及FFHQ和CelebA-Test中的高质量人脸图像，构建了用于UDC人脸修复的训练数据集FFHQ-P/T和测试数据集CelebA-Test-P/T。我们提出了一种新颖的字典引导Transformer网络DGFormer。通过将面部组件字典与UDC图像特性引入修复过程，DGFormer能够解决UDC场景下的盲脸修复问题。实验表明，我们的DGFormer和UDC-DMNet均达到了最先进的性能。