Infrared and visible image fusion method of dual NSCT and PCNN

To solve the problem that the details of fusion images are not retained well and the information of feature targets is incomplete, we proposed a new fusion method of infrared (IR) and visible (VI) image—IR and VI image fusion method of dual non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN). The method makes full use of the flexible multi-resolution and multi-directional of NSCT, and the global coupling and pulse synchronization excitation characteristics of PCNN, effectively combining the features of IR image with the texture details of VI image. Experimental results show that the algorithm can combine IR and VI image features well. At the same time, the obtained fusion image can better display the texture information of image. The fusion performance in contrast, detail information and other aspects is better than the classical fusion algorithm, which has better visual effect and evaluation index.

[1]  Aamir Saeed Malik,et al.  Near-infrared vessels image enhancement using segmentation and fusion technique , 2015, 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[2]  Jin Longxu,et al.  A novel fusion framework of visible light and infrared images based on singular value decomposition and adaptive DUAL-PCNN in NSST domain , 2018 .

[3]  Xiaohua Qiu,et al.  Infrared and Visual Image Fusion Based on NSST and Improved PCNN , 2018, Journal of Physics: Conference Series.

[4]  Yang Lei,et al.  Novel fusion method for visible light and infrared images based on NSST–SF–PCNN , 2014 .

[5]  Moulay A. Akhloufi,et al.  Multimodal Registration and Fusion for 3D Thermal Imaging , 2015 .

[6]  Manjit Kaur,et al.  Efficient prediction of drug-drug interaction using deep learning models. , 2020, IET systems biology.

[7]  Multi‐focus image fusion through DCNN and ELM , 2018, Electronics Letters.

[8]  Zhenhong Jia,et al.  A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain , 2015 .

[9]  Chunhui Zhao,et al.  A fast fusion scheme for infrared and visible light images in NSCT domain , 2015 .

[10]  Shaowen Yao,et al.  A survey of infrared and visual image fusion methods , 2017 .

[11]  Dilbag Singh,et al.  Improved Particle Swarm Optimization Based Adaptive Neuro-Fuzzy Inference System for Benzene Detection , 2018 .

[12]  Yan Huang,et al.  Infrared and Visible Image Fusion Based on Different Constraints in the Non-Subsampled Shearlet Transform Domain , 2018, Sensors.

[13]  Yan Feng,et al.  A fusion algorithm for infrared and visible based on guided filtering and phase congruency in NSST domain , 2017 .

[14]  Jiayi Ma,et al.  Infrared and visible image fusion via gradient transfer and total variation minimization , 2016, Inf. Fusion.

[15]  Jun Huang,et al.  Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model , 2018, Sensors.

[16]  Zhizhong Fu,et al.  Infrared and visible images fusion based on RPCA and NSCT , 2016 .

[17]  Yu Xue,et al.  NSCT-PCNN image fusion based on image gradient motivation , 2018, IET Comput. Vis..

[18]  Dilbag Singh,et al.  Fusion of medical images using deep belief networks , 2019, Cluster Computing.

[19]  Gehao Sheng,et al.  Infrared and Visible Image Fusion of Electric Equipment Using FDST and DC-PCNN , 2018, 2018 Condition Monitoring and Diagnosis (CMD).

[20]  Zheng Liu,et al.  Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain , 2013, IEEE Transactions on Multimedia.

[21]  Yang Lei,et al.  Fusion method for infrared and visible images based on improved quantum theory model , 2016, Neurocomputing.

[22]  Vaishali,et al.  Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks , 2020, European Journal of Clinical Microbiology & Infectious Diseases.

[23]  Feng Li,et al.  Gesture recognition algorithm based on image information fusion in virtual reality , 2019, Personal and Ubiquitous Computing.

[24]  Dilbag Singh,et al.  Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring , 2017, Neural Computing and Applications.

[25]  Jingyu Hou,et al.  Multimodal sensor medical image fusion based on nonsubsampled shearlet transform and S-PCNNs in HSV space , 2018, Signal Process..

[26]  Baojun Zhao,et al.  Fast and Accurate Spatiotemporal Fusion Based Upon Extreme Learning Machine , 2016, IEEE Geoscience and Remote Sensing Letters.

[27]  Piyush Kumar Shukla,et al.  Deep Transfer Learning Based Classification Model for COVID-19 Disease , 2020, IRBM.

[28]  Srinivasu Polinati,et al.  A Review on Multi-Model Medical Image Fusion , 2019, 2019 International Conference on Communication and Signal Processing (ICCSP).