Abstract
In this paper, we propose a novel low-light image enhancement method aimed at improving the performance of recognition models. Despite recent advances in deep learning, the recognition of images under low-light conditions remains a challenge. Although existing low-light image enhancement methods have been developed to improve image visibility for human vision, they do not specifically focus on enhancing recognition model performance. Our proposed low-light image enhancement method consists of two key modules: the Global Enhance Module, which adjusts the overall brightness and color balance of the input image, and the Pixelwise Adjustment Module, which refines image features at the pixel level. These modules are trained to enhance input images to improve downstream recognition model performance effectively. Notably, the proposed method can be applied as a frontend filter to improve low-light recognition performance without requiring retraining of downstream recognition models. Experimental results demonstrate that our method improves the performance of pretrained recognition models under low-light conditions and its effectiveness.
Abstract (translated)
在这篇论文中，我们提出了一种新颖的低光图像增强方法，旨在提高识别模型的性能。尽管深度学习领域取得了诸多进展，但在低光照条件下进行图像识别仍然是一项挑战。虽然现有的低光图像增强方法已开发出来以改善人类视觉下的图像可见性，但它们并未专门针对提升识别模型的性能而设计。 我们提出的低光图像增强方法包含两个关键模块：全局增强模块（Global Enhance Module），用于调整输入图像的整体亮度和色彩平衡；像素级调整模块（Pixelwise Adjustment Module），用于在像素级别细化图像特征。这些模块经过训练，能够增强输入图像以有效提升下游识别模型的性能。 值得注意的是，所提出的方法可以作为前端过滤器使用，在不重新训练下游识别模型的情况下改善低光条件下的识别性能。实验结果表明，我们的方法能够提高预训练识别模型在低光照条件下的表现，并展示了其有效性。
URL
https://arxiv.org/abs/2501.04210
