Exploring in Extremely Dark: Low-Light Video Enhancement with Real Events

Xicong Wang, Huiyuan Fu, Jiaxuan Wang, Xin Wang, Heng Zhang, Huadong Ma

Abstract: Due to the limitations of sensor, traditional cameras struggle to capture details within extremely dark areas of videos. The absence of such details can significantly impact the effectiveness of low-light video enhancement. In contrast, event cameras offer a visual representation with higher dynamic range, facilitating the capture of motion information even in exceptionally dark conditions. Motivated by this advantage, we propose the Real-Event Embedded Network for low-light video enhancement. To better utilize events for enhancing extremely dark regions, we propose an Event-Image Fusion module, which can identify these dark regions and enhance them significantly. To ensure temporal stability of the video and restore details within extremely dark areas, we design unsupervised temporal consistency loss and detail contrast loss. Alongside the supervised loss, these loss functions collectively contribute to the semi-supervised training of the network on unpaired real data. Experimental results on synthetic and real data demonstrate the superiority of the proposed method compared to the state-of-the-art methods. Our codes will be publicly available.

Primary Subject Area: [Content] Media Interpretation

Secondary Subject Area: [Content] Multimodal Fusion

Relevance To Conference:

We propose the Real-Event Embedded Network to leverage real events for addressing low-light enhancement challenges. To the best of our knowledge, it is the first exploration of real events in this field.

We propose the Event-Image Fusion module, which can identify extremely dark regions and utilize event to enhance them.

We design unsupervised temporal consistency loss and detail contrast loss, aiming to maintain temporal stability of videos and restore details in extremely dark regions.

Extensive experiments on both real and synthetic datasets demonstrate that our approach outperforms state-of-the-art methods in low-light video and image enhancement.

Submission Number: 3623