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

• URL: http://arxiv.org/abs/2502.21120v1
• Date: Fri, 28 Feb 2025 14:55:37 GMT
• Title: SEE: See Everything Every Time -- Adaptive Brightness Adjustment for Broad Light Range Images via Events
• Authors: Yunfan Lu, Xiaogang Xu, Hao Lu, Yanlin Qian, Pengteng Li, Huizai Yao, Bin Yang, Junyi Li, Qianyi Cai, Weiyu Guo, Hui Xiong,
• Abstract summary: Event cameras, with a high dynamic range exceeding $120dB$, significantly outperform traditional embedded cameras.<n>We propose a novel research question: how to employ events to enhance and adaptively adjust the brightness of images captured under broad lighting conditions.<n>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.
• Score: 53.79905461386883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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