Systems – Chundoong
Datasets – UDC-SIT
Software (GitHub) – SnuQS / SnuRHAC / SOFF / HUM / SNU NPB 2019 Suite / SnuCL / SNU-SAMSUNG OpenCL framework / SFMalloc / FaCSim / COMIC
UDC-SIT Under Display Camera’s Still Images by Thunder Research Group
Overview
Under Display Camera (UDC) is a novel imaging system that mounts a digital camera lens beneath a display panel with the panel covering the camera. However, the display panel causes severe degradation to captured images, such as low transmittance, blur, noise, and flare. The restoration of UDC-degraded images is challenging because of the unique luminance and diverse patterns of flares. Existing UDC dataset studies focus on unrealistic or synthetic UDC degradation rather than real-world UDC images. In this paper, we propose a real-world UDC dataset called UDC-SIT. To obtain the non-degraded and UDC-degraded images for the same scene, we propose an image-capturing system and an image alignment technique that exploits discrete Fourier transform (DFT) to align a pair of captured images. UDC-SIT also includes comprehensive annotations missing from other UDC datasets, such as light source, day/night, indoor/outdoor, and flare components (e.g., shimmers, streaks, and glares). We compare UDC-SIT with four existing representative UDC datasets and present the problems with existing UDC datasets. To show UDC-SIT’s effectiveness, we compare UDC-SIT and a representative synthetic UDC dataset using five representative learnable image restoration models. The result indicates that the models trained with the synthetic UDC dataset are impractical because the synthetic UDC dataset does not reflect the actual characteristics of UDC-degraded images. UDC-SIT can enable further exploration in the UDC image restoration area and provide better insights into the problem. UDC-SIT is available at: https://github.com/mcrl/UDC-SIT.
Publications
Please use the citation below to reference UDC-SIT.
- [NeurIPS] Kyusu Ahn, Byeonghyun Ko, HyunGyu Lee, Chanwoo Park, and Jaejin Lee. UDC-SIT: A Real-World Dataset for Under-Display Cameras. NeurIPS 2023: Proceedings of the 37th Conference on Neural Information Processing Systems Datasets and Benchmarks Track, New Orleans, Louisiana, USA, December 2023. To appear.
Download
If you would like to download the UDC-SIT, please fill out the following form and click the download button. An email containing the download URL will be sent to the email address.
Datasets and Benchmarks
Contributors
- Authors: Kyusu Ahn, Byeonghyun Ko, HyunGyu Lee, Chanwoo Park, and Jaejin Lee
- Additional contributors: Woojin Kim, Gyuseong Lee, Dongyoung Lee, Sangsoo Im, Gwangho Choi, Gyeongje Jo, Yeonkyoung So, Jiheon Seok, Jaehwan Lee, Donghun Choi, and Daeyoung Park
Contact and Bug Report
E-mail: udcsit@aces.snu.ac.kr
License
Copyright (C) 2023 Seoul National University. All rights reserved.
UDC-SIT 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 as well as the members of the Thunder Research
Group at Seoul National University, including Woojin Kim, Gyuseong Lee, Dongyoung Lee, Sangsoo Im,
Gwangho Choi, Gyeongje Jo, Yeonkyoung So, Jiheon Seok, Jaehwan Lee, Donghun Choi, and Daeyoung
Park, on behalf of universities and research institutions.