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Received:January 18, 2021 Revised:February 23, 2021
Received:January 18, 2021 Revised:February 23, 2021
中文摘要: 为了进一步提升红外和可见光图像的融合效果, 提出了一种基于多尺度卷积算子和密集连接网络的图像融合模型. 该模型首先使用多尺度卷积算子计算图像的直接多尺度特征, 然后使用密集连接网络计算图像的间接多尺度特征. 为了得到图像像素信息在不同尺度下的融合权重, 通过叠加的方式将各个尺度密集连接网络的输出进行融合, 并使用活动图方法计算两类图像的融合权重, 最后根据权重计算结果得到融合图像, 实验在THO数据集和CMA数据集获得较好的识别率.
Abstract:To further improve the fusion effect of visual and infrared images, this paper proposes an image fusion model based on multi-scale convolution operators and DenseNet. This model first uses multi-scale convolution operators to get the direct multi-scale features of images. Then, the DenseNet is used to calculate the indirect multi-scale features of images. To get the fusion weights of image pixel information on different scales, this paper fuses the DenseNet on different scales in a stacking manner, and the fusion weights of the two kinds of images can be derived by activity graphs. At last, the fused image is derived according to the fusion weights. The experimental results show that the recognition rate is high on the THO and CMA sets.
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基金项目:中国社会科学院大学校级科研项目(0016); 国家自然科学基金(61602486)
引用文本：
盖赟,荆国栋.多尺度密集网络在红外和可见光图像融合应用.计算机系统应用,2021,30(11):336-341
GE Yun,JING Guo-Dong.Application of Multi-Scale DenseNet in Image Fusion for Visual Image and Infrared Image.COMPUTER SYSTEMS APPLICATIONS,2021,30(11):336-341
盖赟,荆国栋.多尺度密集网络在红外和可见光图像融合应用.计算机系统应用,2021,30(11):336-341
GE Yun,JING Guo-Dong.Application of Multi-Scale DenseNet in Image Fusion for Visual Image and Infrared Image.COMPUTER SYSTEMS APPLICATIONS,2021,30(11):336-341
