基于多注意力特征融合的轻量级低照度图像增强方法
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1. 南京邮电大学物联网学院,南京210003;2. 南京邮电大学通信与信息工程学院,南京210003

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Lightweight Low-light Image Enhancement Method Based on Multi-attention Feature Fusion
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1. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;2. School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

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    摘要:

    低照度图像增强是将光照不足条件下获取的图像恢复成正常曝光的图像,现有低照度图像增强算法大多通过设计复杂的网络结构获取良好的增强效果,计算效率较低;增强后的图像仍会存在噪声增多、色彩失真、细节丢失等问题,影响视觉感知和后续高级视觉任务。为此,本文提出一种基于多注意力特征融合的轻量级低照度图像增强方法。采用简单门控注意力模块对低照度图像全局特征进行高效提取,通过简化通道注意力及门控单元减少计算开销并保留图像细节信息;采用多注意力融合模块对全局特征及局部接收场提取的局部特征进行信息整合,通过像素注意力强化通道注意力与空间注意力对于全局及局部特征的表征,更好地恢复图像色彩、抑制噪声。此外,使用联合损失函数对增强任务进行约束,在真实数据集上的广泛实验表明,本文方法的性能超过了当前先进的低照度图像增强方法,并具有良好的计算效率和泛化能力。

    Abstract:

    Low Light Image Enhancement, which is to restore the image acquired under the condition of insufficient light to the normal exposure image. Most of the existing low-light image enhancement algorithms obtain good enhancement effect by designing complex network structure, and the computational efficiency is low. The enhanced image will still have problems such as increased noise, color distortion and detail loss, which will affect visual perception and subsequent advanced visual tasks. Therefore, a lightweight low-light image enhancement method based on multi-attention feature fusion is proposed in this paper. Simple gate attention module is used to extract the global features of low-light images effectively, and the computational overhead is reduced and image details are preserved by simplifying the channel attention and gating unit. The multi-attention fusion module is used to integrate the information of global features and local features extracted from local receiving fields, and enhance the representation of channel attention and spatial attention to global and local features through pixel attention, so as to better restore image color and suppress noise. In addition, the joint loss function is used to constrain the enhancement task, and extensive experiments on real data sets show that the performance of the proposed method exceeds the current advanced low-light image enhancement methods, and has good computational efficiency and generalization ability.

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刘 艺,朱佳慧,郑涤尘,张登银.基于多注意力特征融合的轻量级低照度图像增强方法[J].数据采集与处理,,():

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  • 在线发布日期: 2025-07-04