海南师范大学学报（自然科学版）2024，Vol.37Issue(1)：37-45,9.DOI:10.12051/j.issn.1674-4942.2024.01.005
基于照明感知和密集网络的红外与可见光图像融合
Infrared and Visible Image Fusion Based on Illumination Perception and Dense Net
摘要
Abstract
Aiming at the problems of ignoring illumination imbalance,low contrast and texture detail loss in existing meth-ods,this paper proposed a fusion method of infrared and visible image based on illumination perception and dense network.Firstly,the illumination probability was obtained from the visible image and the illumination perception weight was calcu-lated to guide the training network.The adaptive information retention of the source image was calculated by the feature ex-traction and information measurement module to maintain the adaptive similarity between the fusion result and the source image.At the same time,the illumination perception loss and the similarity constraint loss function enabled the model to generate all-weather fusion images containing significant objects and rich texture details in terms of structure,contrast and brightness.In this study,two public data sets,TNO and MSRS,were used for subjective and objective assessment.The ex-perimental results show that this study can make up for the defect of illumination imbalance,and effectively retain more tex-ture details of visible images while preserving more infrared targets.关键词
图像融合/红外图像/可见光图像/照明感知/密集网络Key words
image fusion/infrared image/visible image/illumination perception/dense net分类
信息技术与安全科学引用本文复制引用
张杰,许光宇,陈浩宇..基于照明感知和密集网络的红外与可见光图像融合[J].海南师范大学学报（自然科学版）,2024,37(1):37-45,9.基金项目
国家自然科学基金项目(61471004) （61471004）
安徽理工大学研究生创新基金(2022CX2125) （2022CX2125）
