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.

翻译：事件相机具备超过$120dB$的高动态范围，显著优于传统嵌入式相机，能够在包括低光与高光在内的多种光照条件下稳定记录细节变化信息。然而，当前利用事件数据的研究主要集中于低光图像增强，忽视了在更广泛光照条件（如正常或高照度）下的图像增强与亮度调整。基于此，我们提出一个新的研究问题：如何利用事件数据来增强并自适应调整宽泛光照条件下捕获图像的亮度？为探究此问题，我们首先构建了一个新数据集SEE-600K，包含202个场景下的610,126张图像及对应事件数据，每个场景平均涵盖四种光照条件，照度变化范围超过1000倍。随后，我们提出一个通过提示词有效利用事件数据以实现图像亮度平滑调整的框架。该框架通过传感器模式捕捉色彩，利用交叉注意力将事件建模为亮度字典，并调整图像动态范围以形成宽光照范围表示，最终根据亮度提示在像素级别进行解码。实验结果表明，我们的方法不仅在低光增强数据集上表现优异，在使用SEE-600K数据集的宽光照范围图像增强任务中也展现出鲁棒性能。此外，本方法支持像素级亮度调整，为后处理提供了灵活性，并启发了更多成像应用。数据集与源代码已公开于：https://github.com/yunfanLu/SEE。