丁子扬,谢鹏鹏,李千帆,杜大志,苏美霖

• 1:广西科技大学机械与汽车工程学院

摘要(Abstract)：

深度学习方法被广泛应用于弱光图像增强领域，并取得了良好的效果。弱光图像增强的主要目的是恢复图像中目标区域的可见性。然而，传统方法往往忽略噪声和伪影等问题，导致增强后的图像质量显著下降。针对此问题，本文提出了一种基于YCbCr色彩空间的无监督学习弱光图像增强方法，该方法将弱光图像增强分为2个子网络进行处理。首先，设计了一个亮度调整网络（brightness adjustment network, BAN），并引入亮度增强残差块（luminance enhancement residual, LER）对图像的亮度分量进行调整。其次，通过色彩恢复网络（color recovery network, CRN）增强CbCr通道，该网络可捕捉色度通道的详细信息，并整合全局和局部特征信息。最后，增强后的亮度和色度在通道维度上进行拼接，并通过色彩空间转换得到最终的RGB图像。实验结果表明，该方法能更好地处理各种类型的弱光图像，性能优于目前大多数弱光图像增强方法。在4个公开数据集上的定量与定性评估（包括有参考/无参考图像质量评价）显示，本文方法相比于现有算法具有明显优势。

关键词(KeyWords)： YCbCr色彩空间;RGB图像;弱光图像增强;无监督学习

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目（52202491）资助

作者(Author): 丁子扬,谢鹏鹏,李千帆,杜大志,苏美霖

DOI: 10.16375/j.cnki.cn45-1395/t.2026.03.008

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