SEE: See Everything Every Time -- Adaptive Brightness Adjustment for Broad Light Range Images via Events
Journal:
arXiv
Published Date:
Feb 28, 2025
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.
