Quanta Video Restoration

TL;DR

QUIVER is an end-to-end trainable neural network designed for restoring high-speed, low-light, and noisy videos from single-photon sensors, outperforming existing methods on simulated and real data.

Contribution

The paper introduces QUIVER, a novel deep learning framework for quanta video restoration, and provides a new high-speed dataset I2-2000FPS for training and evaluation.

Findings

QUIVER significantly outperforms existing quanta restoration methods.

The new dataset I2-2000FPS enables better training and testing.

QUIVER effectively handles low-bit, noisy, and motion-corrupted video data.

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-