为提升识别模型在暗光下的表现，提出全局与像素级优化的增强方法。
Recognition-Oriented Low-Light Image Enhancement based on Global and Pixelwise Optimization
- 分全局调整与像素级精修两模块，协同提升图像识别可用性。
- 显著提升预训练识别模型在暗光条件下的准确率，无需重训下游模型。
- 可作为通用前端滤镜，适用于各类低光识别任务。
本文提出一种面向识别任务的暗光图像增强新方法，旨在提升识别模型在低光照条件下的性能。尽管深度学习取得进展，但低光环境下的图像识别仍是挑战。现有增强方法多针对人眼视觉优化，未聚焦于提升识别模型表现。所提方法包含两个核心模块：全局增强模块用于调节图像整体亮度与色彩平衡；像素级调整模块则在像素层面细化图像特征。两模块联合训练，以优化输入图像，从而有效提升下游识别模型性能。值得注意的是，该方法可作为前端滤镜直接使用，无需重新训练下游识别模型。实验结果表明，该方法显著提升了预训练识别模型在低光条件下的表现。
原文摘要 · Abstract (English)
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.
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