SEE: See Everything Every Time -- Adaptive Brightness Adjustment for Broad Light Range Images via Events

TL;DR

This paper introduces a novel framework utilizing event camera data to adaptively enhance and adjust image brightness across a broad range of lighting conditions, supported by a new extensive dataset.

Contribution

It presents a new dataset SEE-600K and a framework that uses events for smooth brightness adjustment and broad light-range image enhancement.

Findings

Effective brightness adjustment across diverse lighting conditions.

Robust performance on broad light-range image enhancement.

Enables pixel-level brightness control for post-processing.

Abstract

Event cameras, with a high dynamic range exceeding $120dB$, significantly outperform traditional embedded cameras, robustly recording detailed changing information under various lighting conditions, including both low- and high-light situations. However, recent research on utilizing event data has primarily focused on low-light image enhancement, neglecting image enhancement and brightness adjustment across a broader range of lighting conditions, such as normal or high illumination. Based on this, we propose a novel research question: how to employ events to enhance and adaptively adjust the brightness of images captured under broad lighting conditions? To investigate this question, we first collected a new dataset, SEE-600K, consisting of 610,126 images and corresponding events across 202 scenarios, each featuring an average of four lighting conditions with over a 1000-fold variation in illumination. Subsequently, we propose a framework that effectively utilizes events to smoothly adjust image brightness through the use of prompts. Our framework captures color through sensor patterns, uses cross-attention to model events as a brightness dictionary, and adjusts the image's dynamic range to form a broad light-range representation (BLR), which is then decoded at the pixel level based on the brightness prompt. Experimental results demonstrate that our method not only performs well on the low-light enhancement dataset but also shows robust performance on broader light-range image enhancement using the SEE-600K dataset. Additionally, our approach enables pixel-level brightness adjustment, providing flexibility for post-processing and inspiring more imaging applications. The dataset and source code are publicly available at: https://github.com/yunfanLu/SEE.