UDC-VIT: A Real-World Video Dataset for Under-Display Cameras
This addresses a data gap for researchers and developers working on UDC imaging, though it is incremental as it builds upon existing datasets by focusing on real-world video.
The authors tackled the lack of real-world video datasets for Under-Display Cameras (UDCs) by introducing UDC-VIT, a dataset that includes human motions for facial recognition, and demonstrated that models trained on synthetic datasets are ineffective for UDC restoration, with results showing the importance of accurate degradation modeling for face recognition.
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.