Deep Video Restoration for Under-Display Camera

• URL: http://arxiv.org/abs/2309.04752v1
• Date: Sat, 9 Sep 2023 10:48:06 GMT
• Title: Deep Video Restoration for Under-Display Camera
• Authors: Xuanxi Chen, Tao Wang, Ziqian Shao, Kaihao Zhang, Wenhan Luo, Tong Lu, Zikun Liu, Tae-Kyun Kim, Hongdong Li
• Abstract summary: We propose a GAN-based generation pipeline to simulate the realistic UDC degradation process. We build the first large-scale UDC video restoration dataset called PexelsUDC. We propose a novel transformer-based baseline method that adaptively enhances degraded videos.
• Score: 98.17505013737446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Images or videos captured by the Under-Display Camera (UDC) suffer from severe degradation, such as saturation degeneration and color shift. While restoration for UDC has been a critical task, existing works of UDC restoration focus only on images. UDC video restoration (UDC-VR) has not been explored in the community. In this work, we first propose a GAN-based generation pipeline to simulate the realistic UDC degradation process. With the pipeline, we build the first large-scale UDC video restoration dataset called PexelsUDC, which includes two subsets named PexelsUDC-T and PexelsUDC-P corresponding to different displays for UDC. Using the proposed dataset, we conduct extensive benchmark studies on existing video restoration methods and observe their limitations on the UDC-VR task. To this end, we propose a novel transformer-based baseline method that adaptively enhances degraded videos. The key components of the method are a spatial branch with local-aware transformers, a temporal branch embedded temporal transformers, and a spatial-temporal fusion module. These components drive the model to fully exploit spatial and temporal information for UDC-VR. Extensive experiments show that our method achieves state-of-the-art performance on PexelsUDC. The benchmark and the baseline method are expected to promote the progress of UDC-VR in the community, which will be made public.

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