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半LLIE:基于Mamba的低光增强的半监督对比学习

Guanlin Li¹, Ke Zhang², Ting Wang²

¹College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shannxi, China; School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, 710072, Shannxi, China.

Neural networks : the official journal of the International Neural Network Society

|September 3, 2025

概括

这项研究引入了半LLIE,这是一个半监督的低光图像增强框架 (LLIE),有效地使用未配对的数据. 它克服了照明转移和细节恢复的局限性,以获得更好的图像质量.

科学领域:

计算机视觉
人工智能
图像处理

背景情况:

低光图像增强 (LLIE) 已经取得了显著的进步.
在LLIE的一个主要挑战是缺乏配对的低光和正常光训练数据.
现有的方法难以实现现实的照明转移和在黑暗区域的细节恢复.

研究的目的:

提出一个新的半监控框架,半LLIE,用于低光图像增强.
解决利用未配对数据和恢复细节的现有方法的局限性.
为了提高颜色的自然性和增强低光图像的纹理.

主要方法:

一个半监督框架 (半LLIE) 通过平均教师范式使用未配对的低光和正常光照图像.
一个具有语义意识的对比损失,利用视觉语言表示来对准照明.
一个基于Mamba的骨干,具有多级特征学习,用于增强细节恢复.
基于RAM的感知损失用于语义层面的纹理增强.

主要成果:

半LLIE在低光图像增强方面表现优于现有方法.
拟议的方法可以提高定量和定性指标.
增强了色彩的自然性,并在黑暗区域恢复了细粒度的细节.

结论:

半LLIE框架有效地解决了有限的配对数据在低光图像增强方面的挑战.
新的损失功能和骨干架构显著改善了照明传输和细节恢复.
半LLIE为高质量的低光图像增强提供了有前途的解决方案.

关键词:

进行对比学习图像增强在低光下具有语义意识半监督学习

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