Seeing the Unseen: Zooming in the Dark with Event Cameras

TL;DR

This paper introduces RetinexEVSR, a novel event-driven framework for low-light video super-resolution that effectively leverages event signals and Retinex priors to enhance detail recovery and reduce artifacts.

Contribution

RetinexEVSR is the first to integrate event data with Retinex-inspired priors for low-light video super-resolution, employing a bidirectional fusion strategy and illumination-guided enhancement modules.

Findings

Achieves state-of-the-art performance on three datasets.

Up to 2.95 dB gain on SDSD benchmark.

Reduces runtime by 65% compared to previous methods.

Abstract

This paper addresses low-light video super-resolution (LVSR), aiming to restore high-resolution videos from low-light, low-resolution (LR) inputs. Existing LVSR methods often struggle to recover fine details due to limited contrast and insufficient high-frequency information. To overcome these challenges, we present RetinexEVSR, the first event-driven LVSR framework that leverages high-contrast event signals and Retinex-inspired priors to enhance video quality under low-light scenarios. Unlike previous approaches that directly fuse degraded signals, RetinexEVSR introduces a novel bidirectional cross-modal fusion strategy to extract and integrate meaningful cues from noisy event data and degraded RGB frames. Specifically, an illumination-guided event enhancement module is designed to progressively refine event features using illumination maps derived from the Retinex model, thereby suppressing low-light artifacts while preserving high-contrast details. Furthermore, we propose an event-guided reflectance enhancement module that utilizes the enhanced event features to dynamically recover reflectance details via a multi-scale fusion mechanism. Experimental results show that our RetinexEVSR achieves state-of-the-art performance on three datasets. Notably, on the SDSD benchmark, our method can get up to 2.95 dB gain while reducing runtime by 65% compared to prior event-based methods. Code: https://github.com/DachunKai/RetinexEVSR.