Software and Systems

Systems – Chundoong
Datasets – UDC-SIT / UDC-VIT
Software (GitHub) – SnuQS / SnuRHAC / SOFF / HUM / SNU NPB 2019 Suite / SnuCL / SNU-SAMSUNG OpenCL framework / SFMalloc / FaCSim / COMIC

UDC-VIT Under Display Camera’s VIdeo by Thunder Research Group

Overview

Under Display Camera (UDC) is an advanced imaging system that places a digital camera lens underneath a display panel, effectively concealing the camera. However, 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. Despite extensive research on UDC images and their restoration models, studies on videos have yet to be significantly explored. While two UDC video datasets exist, they primarily focus on unrealistic or synthetic UDC degradation rather than real-world UDC degradation. In this paper, we propose a real-world UDC video dataset called UDC-VIT. Unlike existing datasets, only UDC-VIT exclusively includes human motions that target facial recognition. We propose a video-capturing system to simultaneously acquire non-degraded and UDC-degraded videos of the same scene. 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 enables further exploration in the UDC video restoration and offers better insights into the challenge. UDC-VIT is available at our project site.

Publications

Please use the citation below to reference UDC-VIT.

[arXiv] Kyusu Ahn, JiSoo Kim, Sangik Lee, HyunGyu Lee, Byeonghyun Ko, Chanwoo Park, and Jaejin Lee. UDC-VIT: A Real-World Video Dataset for Under-Display Cameras. arXiv preprint arXiv:2501.18545 (2025).

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Datasets and Benchmarks

Contributors

Authors: Kyusu Ahn, JiSoo Kim, Sangik Lee, HyunGyu Lee, Byeonghyun Ko, Chanwoo Park, and Jaejin Lee.

Contact and Bug Report

E-mail: udcvit@aces.snu.ac.kr

UDC-VIT dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
International (CC BY-NC-SA 4.0). This is a human-readable summary of (and not a substitute for)
the license. Disclaimer.

You are free to:
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Read the full license text at: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode

Contact information:
Thunder Research Group
Graduate School of Data Science
Seoul National University, Seoul 151-744, Korea

https://thunder.snu.ac.kr

Contributors:
The dataset is created by the authors of the paper.