双树复小波与多尺度特征的红外图像融合增强

魏艳平

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双树复小波与多尺度特征的红外图像融合增强

光学图像处理 | 更新时间：2026-03-24

- 双树复小波与多尺度特征的红外图像融合增强
- Infrared image fusion enhancement using dual tree complex wavelet and multi-scale features
- 光学技术 2026年52卷第2期页码：245-251
- 作者机构：
  
  南昌应用技术师范学院电子与信息工程学院，江西南昌330108
- 作者简介：
  
  魏艳平（1982— ），男，副教授，从事计算机图形学、信号和信息处理与嵌入式系统开发方面的研究。weiyanping@nncat.edu.cn
- 基金信息：
  
  江西省教育厅科学技术研究项目(GJJ2403103);江西省高等学校教学改革研究课题(JXJG-24-42-4)
- DOI：
  中图分类号： TP391.41
- 收稿：2025-11-19，
  
  修回：2025-12-26，
  
  纸质出版：2026-03-20
- 稿件说明：
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魏艳平.双树复小波与多尺度特征的红外图像融合增强[J].光学技术,2026,52(02):245-251.

WEI Yanping.Infrared image fusion enhancement using dual tree complex wavelet and multi-scale features[J].OPTICAL TECHNIQUE,2026,52(02):245-251.
魏艳平.双树复小波与多尺度特征的红外图像融合增强[J].光学技术,2026,52(02):245-251. DOI：

WEI Yanping.Infrared image fusion enhancement using dual tree complex wavelet and multi-scale features[J].OPTICAL TECHNIQUE,2026,52(02):245-251. DOI：

摘要

针对红外图像在亮度分布、对比度以及细节清晰度上的不足，提出一种基于双树复小波与多尺度特征的红外图像融合增强方法。其中，利用自然对数的斜率递减的特点，提出一种自适应的对数斜率变换函数，分别对每一像素进行变换拉伸，优化场景的亮度分布和对比度；在对数斜率变换的基础上，提出一种基于自适应系数的非锐化掩蔽方法，提升图像的边缘和细节的清晰度；借助双树复小波变换的多尺度与多方向特性，分别将两增强图像分解为多尺度、多方向的子带，然后根据自定义的亮度权重图与细节权重图，对相同尺度、相同方向的子带进行加权融合，最后对融合的子带进行双树复小波反变换，获得效果理想的红外图像。从视觉感知和量化数据两方面对提出方法进行性能评估，结果显示提出方法比现有方法具有更好的视觉质量和更高的平均梯度、空间频率和信息熵，因此提出方法能够更好地应用于红外成像领域。

Abstract

Aiming at the shortcomings of infrared images in terms of brightness distribution

contrast

and detail clarity

an infrared image fusion enhancement method based on dual tree complex wavelet and multi-scale features is proposed. Therein

by utilizing the decreasing slope of natural logarithm

an adaptive logarithmic slope transformation function is proposed to enhance the brightness distribution and contrast of the scene by transforming and stretching each pixel separately. On the basis of logarithmic slope transformation

a non-sharpening masking method based on adaptive coefficients is proposed to enhance the clarity of image edges and details. By utilizing the multi-scale and multi-directional properties of the dual tree complex wavelet transform

the two enhanced images are decomposed into multi-scale and multi-directional sub-bands

respectively

and according to the custom brightness weight map and detail weight map

the sub-bands of the same scale and direction are weighted and fused. Finally

the fused sub-bands are subjected to dual tree complex wavelet inverse transform to obtain the ideal infrared image. The performance evaluation of the proposed method was conducted from both visual perception and quantitative data perspectives

and the results showed that the proposed method has better visual quality and higher average gradient

spatial frequency and information entropy than existing methods. Therefore

the proposed method would be a better method in the field of infrared imaging.

关键词

Keywords

references

周辉奎 , 顾牡丹 . 自适应加权直方图均衡化的红外图像增强 [J ] . 光学技术 , 2023 , 49 （ 06 ）: 750 — 755 .

Zhou Huikui , Gu Mudan . Infrared image enhancement based on adaptive weighted histogram equalization [J ] . Optical Technique , 2023 , 49 （ 06 ）: 750 — 755 .

Gao M J , Bai Y , Liao H P , et al . Contrast enhancement algorithm for infrared images based on atmospheric scattering model [J ] . Computers & Electrical Engineering , 2024 , 118 （ 12 ）: 109318 .

罗国强 , 陈家益 . 改进双边滤波与平均γ矫正的图像增强 [J ] . 传感技术学报 , 2022 , 35 （ 05 ）: 644 — 649 .

Luo Guoqiang , Chen Jiayi . Image enhancement based on improved bilateral filtering and average γ correction [J ] . Chinese Journal of Sensors and Actuators , 2022 , 35 （ 05 ）: 644 — 649 .

Lu P , Mu Y , Gu C J , et al . Multi-scale infrared image enhancement based on non-uniform weighted guided filtering [J ] . Optics and Lasers in Engineering , 2025 , 186 : 108797 .

Song H F , Wang Z Q , Cao W X , et al . Infrared image enhancement based on guided filtering and adaptive algorithm and its FPGA implementation [J ] . Microwave and Optical Technology Letters , 2025 , 67 （ 1 ）: e70105 .

Chen B Y , Song Y M , Yang G , et al . An infrared image enhancement method based on multi-channel feature fusion network [J ] . Optik , 2025 , 327 : 172306 .

蔡美芳 , 万里勇 . 基于自适应Gamma校正的红外图像增强算法 [J ] . 光学技术 , 2022 , 48 （ 04 ）: 486 — 491 .

Cai Meifang , Wan Liyong . Infrared image enhancement algorithm based on adaptive Gamma correction [J ] . Optical Technique , 2022 , 48 （ 04 ）: 486 — 491 .

Yang H T , Tong Y N , Cao Z Q , et al . Infrared image enhancement algorithm based on improved wavelet threshold function and weighted guided filtering [C ] . Journal of Physics: Conference Series , 2023 , 2525 （ 0 ）: 012017 .

Zhang D , Liu Y N , Zhao Y , et al . Algorithm research on detail and contrast enhancement of high dynamic infrared images [J ] . Applied Sciences-Basel , 2023 , 13 （ 23 ）: 12649 .

He Y , Kang S , Li W . et al . Advanced enhancement technique for infrared images of wind turbine blades utilizing adaptive difference multi-scale top-hat transformation [J ] . Scientific Reports , 2024 , 14 : 15604 .

Li Y . Enhancing low-light images with lightweight fused fixed-directional filters network [J ] . IET Image Processing , 2024 , 18 （ 13 ）: 3976 — 3988 .

Pang Z X , Liu G H , Li G S , et al . An infrared image enhancement method via content and detail two-stream deep convolutional neural network [J ] . Infrared Physics & Technology , 2023 , 132 （ Suppl C ）: 104761 .

Zhang X H , Chen G L , Chen Z Y , et al . Multi-scale feature enhancement and fusion network for real-world infrared image denoising [J ] . Infrared Physics & Technology , 2025 , 148 : 105833 .

章联军 , 张鹏 , 陈芬 , 等 . 融合深度学习与多尺度Retinex的水下图像增强方法 [J ] . 光电子·激光 , 2023 , 34 （ 08 ）: 823 — 832 .

Zhang L J , Zhang P , Chen F , et al . Combining deep learning and multi-scale Retinex for underwater images enhancement [J ] . Journal of Optoelectronics·Laser , 2023 , 34 （ 08 ）: 823 — 832 .

Zhang Y Q , Jiang J , Wang Z , et al . Low-light image enhancement method based on retinex theory and dual-tree complex wavelet transform [J ] . Journal of King Saud University Computer and Information Sciences , 2025 , 37 （ 5 ）: 1 — 14 .

Zhou Y , He K , Xu D , et al . A multi-weight fusion framework for infrared and visible image fusion [J ] . Multimedia Tools and Applications , 2024 , 83 , 68931 — 68957 .

Liu X , Ma W , Ma X , et al . LAE-Net: A locally-adaptive embedding network for low-light image enhancement [J ] . Pattern Recognition , 2023 , Suppl C : 109039 .

Cheng H , Long W , Li Y , et al . Two low illuminance image enhancement algorithms based on grey level mapping [J ] . Multimedia Tools and Applications , 2021 , 80 （ 5 ）: 7205 — 7228 .

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