Quanta Video Restoration

• URL: http://arxiv.org/abs/2410.14994v1
• Date: Sat, 19 Oct 2024 05:50:12 GMT
• Title: Quanta Video Restoration
• Authors: Prateek Chennuri, Yiheng Chi, Enze Jiang, G. M. Dilshan Godaliyadda, Abhiram Gnanasambandam, Hamid R. Sheikh, Istvan Gyongy, Stanley H. Chan,
• Abstract summary: We introduce Quanta Video Restoration (QUIVER), an end-to-end trainable network built on the core ideas of classical quanta restoration methods. On simulated and real data, QUIVER outperforms existing quanta restoration methods by a significant margin.
• Score: 12.708095170886313
• License:
• Abstract: The proliferation of single-photon image sensors has opened the door to a plethora of high-speed and low-light imaging applications. However, data collected by these sensors are often 1-bit or few-bit, and corrupted by noise and strong motion. Conventional video restoration methods are not designed to handle this situation, while specialized quanta burst algorithms have limited performance when the number of input frames is low. In this paper, we introduce Quanta Video Restoration (QUIVER), an end-to-end trainable network built on the core ideas of classical quanta restoration methods, i.e., pre-filtering, flow estimation, fusion, and refinement. We also collect and publish I2-2000FPS, a high-speed video dataset with the highest temporal resolution of 2000 frames-per-second, for training and testing. On simulated and real data, QUIVER outperforms existing quanta restoration methods by a significant margin. Code and dataset available at https://github.com/chennuriprateek/Quanta_Video_Restoration-QUIVER-

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