基于迭代引导滤波和双通道自适应脉冲耦合神经网络的红外和可见光图像融合方法

IF 1.8 Q3 REMOTE SENSING International Journal of Image and Data Fusion Pub Date : 2020-08-31 DOI:10.1080/19479832.2020.1814877

Qiufeng Fan, F. Hou, Feng Shi

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引用次数: 1

摘要

摘要为了充分利用源图像的重要特征，提出了一种基于迭代引导滤波和双通道自适应脉冲耦合神经网络的红外与可见光融合方法。通过迭代导向滤波器将输入图像分解为基本层、小尺度层和大尺度层。通过结合像素能量和梯度能量融合底层。然后通过双通道自适应脉冲耦合神经网络融合大尺度层和小尺度层。采用逆混合多尺度分解方法得到融合图像。实验结果表明，与其他多尺度分解方法相比，该方法能更好地分离空间重叠特征，并在融合图像中保留更多细节信息，有效抑制伪影。

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A fusion method for infrared and visible images based on iterative guided filtering and two channel adaptive pulse coupled neural network

ABSTRACT In order to make full use of the important features of the source image, an infrared and visible fusion method based on iterative guided filtering and two-channel adaptive pulse coupled neural network is proposed. The input image is decomposed into basic layer, small scale layer and large scale layer by an iterative guide filter. The base layer is fused by combining pixel energy and gradient energy. Then we fuse the large scale layer and small scale layer via two-channel adaptive pulse coupled neural network. The fused image is obtained by the inverse mixing multi-scale decomposition method. Experimental results show that compared with other multi-scale decomposition methods, the proposed method can better separate spatial overlapping features, and preserve more detailed information in fused image, effectively suppress artefacts.

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).