HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera

• URL: http://arxiv.org/abs/2405.13389v1
• Date: Wed, 22 May 2024 06:51:32 GMT
• Title: HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera
• Authors: Yunfan Lu, Zipeng Wang, Yusheng Wang, Hui Xiong,
• Abstract summary: Continuous space-time super-resolution (C-STVSR) aims to simultaneously enhance resolution and frame rate at an arbitrary scale. We propose a novel C-STVSR framework, called HR-INR, which captures both holistic dependencies and regional motions based on implicit neural representation (INR) We then propose a novel INR-based decoder withtemporal embeddings to capture long-term dependencies with a larger temporal perception field.
• Score: 22.208120663778043
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Continuous space-time video super-resolution (C-STVSR) aims to simultaneously enhance video resolution and frame rate at an arbitrary scale. Recently, implicit neural representation (INR) has been applied to video restoration, representing videos as implicit fields that can be decoded at an arbitrary scale. However, the highly ill-posed nature of C-STVSR limits the effectiveness of current INR-based methods: they assume linear motion between frames and use interpolation or feature warping to generate features at arbitrary spatiotemporal positions with two consecutive frames. This restrains C-STVSR from capturing rapid and nonlinear motion and long-term dependencies (involving more than two frames) in complex dynamic scenes. In this paper, we propose a novel C-STVSR framework, called HR-INR, which captures both holistic dependencies and regional motions based on INR. It is assisted by an event camera, a novel sensor renowned for its high temporal resolution and low latency. To fully utilize the rich temporal information from events, we design a feature extraction consisting of (1) a regional event feature extractor - taking events as inputs via the proposed event temporal pyramid representation to capture the regional nonlinear motion and (2) a holistic event-frame feature extractor for long-term dependence and continuity motion. We then propose a novel INR-based decoder with spatiotemporal embeddings to capture long-term dependencies with a larger temporal perception field. We validate the effectiveness and generalization of our method on four datasets (both simulated and real data), showing the superiority of our method.

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