多尺度特征提取与融合的红外图像增强算法  

Infrared image enhancement algorithm based on multi-scale feature extraction and fusion

作　　者：李牧[1,2] 张一朗 柯熙政 LI Mu;ZHANG Yilang;KE Xizheng(College of Automation and Information Engineering,Xi'an University of Technology,Xi'an 710048,China;Shaanxi Provincial Key Laboratory of Intelligent Collaborative Network,Xi'an 710048,China)

机构地区：[1]西安理工大学自动化与信息工程学院,陕西西安710048 [2]陕西省智能协同网络军民共建重点实验室,陕西西安710048

出　　处：《红外与激光工程》2025年第2期240-253,共14页Infrared and Laser Engineering

基　　金：陕西省教育厅科研计划项目(18JK0341);西安市科技计划项目(2020KJRC0083)。

摘　　要：针对传统的特征融合算法多从单一的尺度上抽取图像的特征,并且在红外图像亮度增强过程中可能导致局部特征信息的丢失与退化而引起红外图像细节分辨率不高的问题,提出了多尺度特征提取与融合的红外图像增强算法,主要由多尺度自适应特征提取模块、亮度增强迭代函数以及特征融合和图像重建模块构成。首先,提出的多尺度自适应特征提取融合模块保存和融合了来自不同卷积层特征的多尺度信息;然后,改进的亮度增强迭代函数使用了融合特征作为逐像素参数,用于红外图像亮度增强;最后,通过提出的特征融合和图像重建模块,增强了特征在网络中的传播能力,并保持了局部信息的完整性。实验结果表明:多尺度特征提取与融合的红外图像增强算法与其它表现较好的网络相比,峰值信噪比、余弦相似度以及信息熵分别提高了3.7%、1.3%、1.6%。且在测试数据集上根据引用的火灾隐患检测算法判断是否存在火灾隐患进行早期火灾检测,其准确率为97.86%,说明了提出的多尺度特征提取与融合的红外图像增强算法的有效性与可行性。Objective In recent years,with the development of infrared sensor technology,many image processing applications based on infrared images have emerged.Infrared images can provide 24/7 information that the human eye can't see.Therefore,infrared image is widely used in monitoring,industry,military and other fields.Unlike visible light images captured by ordinary sensors,infrared images typically have low contrast,contain blurred edges,and a lot of noise.The reason for the low contrast and blurred edges is that usually the foreground and background have similar temperatures.Low contrast and blurred edges will produce low-quality infrared images.In addition,infrared images have a low signal-to-noise ratio due to the infrared sensor and readout circuit of infrared cameras,which leads to low signal and high noise problems that further degrade infrared image quality.And low-quality infrared images can bring many difficulties to further analysis,such as object recognition and image fusion.It is best to use effective infrared image enhancement techniques to produce high-quality infrared images,that is,high contrast,clear detail and less noise.Methods In this paper,an infrared image enhancement algorithm based on multi-scale feature extraction and fusion is proposed.Firstly,a multi-scale adaptive feature extraction fusion module is designed,which fuses convolution features detected at multiple levels based on multi-layer feature fusion to fully preserve details in image reconstruction.The Global Attention Mechanism(GAM)is introduced into the multi-scale adaptive feature extraction fusion module,which can amplify the interdimensional interaction,obtain the features of three dimensions at the same time,avoid information loss,and retain more feature information.Then,the luminanceenhancement iteration function is designed,using the fusion features of different levels as the pixel parameters ofthe iteration function to avoid the problem of local exposure.Finally,a feature fusion and image reconstructionmodule is designed.After t

关 键 词：红外图像 图像增强 深度学习 特征融合 注意力机制 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN911.73[自动化与计算机技术—控制科学与工程]