Infrared and low illumination image fusion based on image features

被引：0
|
作者：
Wang H. [1 ]
Wang C. [1 ]
Fu Q. [1 ]
Han Z. [2 ]
Zhang D. [1 ]
机构：
[1] Department of Electronic and Optical Engineering, Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang
[2] Unit 32356 of PLA, Xining
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 08期
关键词：
image fusion algorithm; infrared image; low illumination image; non-subsampled shearlet transform; pulse-coupled neural network;
D O I：
10.12305/j.issn.1001-506X.2023.08.13
中图分类号：
学科分类号：
摘要：
Aiming at the problem that it is difficult to analyze the target and background information in the scene of infrared images and low illumination images independently, an infrared and low illumination image fusion algorithm based on image features is proposed. Firstly, the infrared and low illumination images are processed to optimize their characteristics respectively. Secondly, the non-subsampled shearlet transform is used to decompose them in high and low frequency. Thirdly, the improved Laplace weighted algorithm is used to fuse the low-frequency images. The improved pulse coupled neural network is used to fuse the high-frequency images. Finally, the fused image is obtained by non-subsampled shearlet inverse transform. Experimental results show that the proposed algorithm can effectively fuse infrared and low illumination images, reduce the impact of noise on the fused image, improve the clarity of the fused image, and the contrast of the image is moderate, which is suifable the visual perception effect of human eyes. © 2023 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：2395 / 2404
页数：9
相关论文
共 31 条
  • [1] XING Y Q, WANG X D, BI K, Et al., Infrared and visible image fusion method based on NSCT and ICA, Systems Engineering and Electronics, 35, 11, pp. 2251-2256, (2013)
  • [2] JIANG Z T, Qf N L L, Et al., Low-light image enhancement method based on MDARNet [J], Journal of Software, 32, 12, pp. 3977-3991, (2021)
  • [3] ZHOU X L, JIANG Z T., Infrared and visible image fusion combining pulse-coupled neural network and guided filtering, Acta Optical Sinica, 39, 11, (2019)
  • [4] LIU K, GUO L, LI H H, Et al., Fusion of infrared and visible light images based on region segmentation, Chinese Journal of Aeronautics, 22, 1, pp. 75-80, (2009)
  • [5] GAI D, SHEN X J, CHEN H P, Et al., Medical image fusion u- smg the PCNN based on IQPSO in NSST domain, IET Image Processing, 14, 9, pp. 1870-1880, (2020)
  • [6] CHEN J, LI X J, LUO L B, Et al., Infrared and visible image fusion based on target-enhanced multiscale transform decomposi- tlon, Information Sciences, 508, pp. 64-78, (2020)
  • [7] BHAT S, KOUNDAL D., Multi-focus image fusion using neu- trosophic based wavelet transform, Applied Soft Computing, 106, (2021)
  • [8] GUO L Q, CAO X, LIU L., Dual-tree biquaternion wavelet transform and its application to color image fusion, Signal Processing, 171, (2020)
  • [9] AGHAMALEKI J A, GHORBAN1 A., Image fusion using dual tree discrete wavelet transform and weights optimization, The Visual Computer, 39, pp. 1181-1191, (2023)
  • [10] ZHAO C H, GUO Y T, WANG Y L., A fast fusion scheme for infrared and visible light images in NSCT domain[J], Infrared Physics and Technology, 72, pp. 266-275, (2015)
1234