# UDC-VIT: A Real-World Video Dataset for Under-Display Cameras

arXiv: https://arxiv.org/abs/2501.18545 · HTML: https://mlsanity.com/paper/2501.18545/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 22 of 22)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization](https://mlsanity.com/paper/2002.11244/) (2020)
- [Flare7K: A Phenomenological Nighttime Flare Removal Dataset](https://mlsanity.com/paper/2210.06570/) (2022)
- [Image Restoration for Under-Display Camera](https://mlsanity.com/paper/2003.04857/) (2020)
- [Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network](https://mlsanity.com/paper/2104.09556/) (2021)
- [Learning Invariant Representation for Unsupervised Image Restoration](https://mlsanity.com/paper/2003.12769/) (2020)
- [Motion Basis Learning for Unsupervised Deep Homography Estimation with Subspace Projection](https://mlsanity.com/paper/2103.15346/) (2021)
- [Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior](https://mlsanity.com/paper/2303.15046/) (2023)
- [Real-World Deep Local Motion Deblurring](https://mlsanity.com/paper/2204.08179/) (2022)
- [ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig](https://mlsanity.com/paper/2004.08513/) (2020)
- [Test-Time Degradation Adaptation for Open-Set Image Restoration](https://mlsanity.com/paper/2312.02197/) (2023)
- [Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera](https://mlsanity.com/paper/2304.06019/) (2023)
- [MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results](https://mlsanity.com/paper/2209.07052/) (2022)
- [UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network](https://mlsanity.com/paper/2209.01809/) (2022)
- [LRDif: Diffusion Models for Under-Display Camera Emotion Recognition](https://mlsanity.com/paper/2402.00250/) (2024)
- [Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer](https://mlsanity.com/paper/2308.10196/) (2023)
- [Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display Camera](https://mlsanity.com/paper/2403.05660/) (2024)
- [Deep Video Restoration for Under-Display Camera](https://mlsanity.com/paper/2309.04752/) (2023)

## Built on by (most cited first, 1 of 1)

- [LUCID: Learning Unified Control for Image Deflaring and Exposure Mastery in Nighttime Photography](https://mlsanity.com/paper/2606.06901/) (2026)

## Abstract (arXiv)

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.