Fusion of Infrared and Visible Light Images Based on Improved Adaptive Dual-Channel Pulse Coupled Neural Network

被引:2
|
作者
Feng, Bin [1 ]
Ai, Chengbo [1 ]
Zhang, Haofei [2 ]
机构
[1] Xian Technol Univ, Sch Optoelect Engn, Xian 710021, Peoples R China
[2] 208 Res Inst China Ordnance Ind, Beijing 102202, Peoples R China
关键词
pulse coupled neural network; infrared and visible light images; image fusion; non-subsampled shearlet transform; PERFORMANCE; PCNN;
D O I
10.3390/electronics13122337
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The pulse-coupled neural network (PCNN), due to its effectiveness in simulating the mammalian visual system to perceive and understand visual information, has been widely applied in the fields of image segmentation and image fusion. To address the issues of low contrast and the loss of detail information in infrared and visible light image fusion, this paper proposes a novel image fusion method based on an improved adaptive dual-channel PCNN model in the non-subsampled shearlet transform (NSST) domain. Firstly, NSST is used to decompose the infrared and visible light images into a series of high-pass sub-bands and a low-pass sub-band, respectively. Next, the PCNN models are stimulated using the weighted sum of the eight-neighborhood Laplacian of the high-pass sub-bands and the energy activity of the low-pass sub-band. The high-pass sub-bands are fused using local structural information as the basis for the linking strength for the PCNN, while the low-pass sub-band is fused using a linking strength based on multiscale morphological gradients. Finally, the fused high-pass and low-pass sub-bands are reconstructed to obtain the fused image. Comparative experiments demonstrate that, subjectively, this method effectively enhances the contrast of scenes and targets while preserving the detail information of the source images. Compared to the best mean values of the objective evaluation metrics of the compared methods, the proposed method shows improvements of 2.35%, 3.49%, and 11.60% in information entropy, mutual information, and standard deviation, respectively.
引用
收藏
页数:16
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