UDC-VIT: A Real-World Video Dataset for Under-Display Cameras
Dept. of Data Science, Seoul National University（数据科学系，首尔国立大学）；Dept. of Computer Science and Engineering, Seoul National University（计算机科学与工程系，首尔国立大学）；Research Center, Samsung Display Co., Ltd.（三星显示公司研究部）；Mobile Display Electronics Development Team, Samsung Display Co., Ltd.（三星显示公司移动显示电子开发团队）
Kyusu Ahn, JiSoo Kim, Sangik Lee, HyunGyu Lee, Byeonghyun Ko, Chanwoo Park, Jaejin Lee
Even though an Under-Display Camera (UDC) is an advanced imaging system, the display panel significantly degrades captured images or videos, introducing low transmittance, blur, noise, and flare issues. Tackling such issues is challenging because of the complex degradation of UDCs, including diverse flare patterns. However, no dataset contains videos of real-world UDC degradation. In this paper, we propose a real-world UDC video dataset called UDC-VIT. Unlike existing datasets, UDC-VIT exclusively includes human motions for facial recognition. We propose a video-capturing system to acquire clean and UDC-degraded videos of the same scene simultaneously. Then, we align a pair of captured videos frame by frame, using discrete Fourier transform (DFT). We compare UDC-VIT with six representative UDC still image datasets and two existing UDC video datasets. Using six deep-learning models, we compare UDC-VIT and an existing synthetic UDC video dataset. The results indicate the ineffectiveness of models trained on earlier synthetic UDC video datasets, as they do not reflect the actual characteristics of UDC-degraded videos. We also demonstrate the importance of effective UDC restoration by evaluating face recognition accuracy concerning PSNR, SSIM, and LPIPS scores. UDC-VIT is available at our official GitHub repository.
State Key Lab for Novel Software Technology, Nanjing University（南京大学现代软件技术国家重点实验室）；Australian National University（澳大利亚国立大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Samsung Research China-Beijing (SRC-B)（三星中国研究院北京分部）；Imperial College London（伦敦帝国学院）
UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）；Shanghai AI Laboratory（上海人工智能实验室）
Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Hong Kong University of Science and Technology（香港科技大学）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Samsung Research China - Beijing (SRC-B)（三星中国研究院北京分部）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳校区）；SAMSUNG Electronics（三星电子）
Deep Atrous Guided Filter for Image Restoration in Under Display Cameras
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Under Display相机为手机制造商提供了一个充满希望的机会，通过将相机定位在半透明OLED屏幕后面来实现无边框显示。不幸的是，这种成像系统由于光衰减和衍射效应而遭受严重的图像退化。在这项工作中，我们提出了深度Atrous引导滤波器（DAGF），一个两阶段，端到端的方法，图像复原在UDC系统。低分辨率网络首先以低分辨率恢复图像质量，随后被引导滤波器网络用作滤波输入以产生高分辨率输出。除了初始下采样，我们的低分辨率网络还使用多个并行的无环卷积来保持空间分辨率，并模拟多尺度处理。我们的方法的能力，直接训练的百万像素图像的结果在显着的性能提高。我们还提出了一个简单的模拟方案来预训练我们的模型并提高性能。我们的整体框架在RLQ-TOD'20 UDC挑战赛中分别在POLED和TOLED显示屏领域排名第二和第五。
