Fusion Algorithm for Near-Infrared and Color Visible Images Based on Tetrolet Transform

A novel system of implementing target identification with hyperspectral imaging system based on acousto-optic tunable filter (AOTF) was proposed. The system consists of lens, AOTF, AOTF driver, CCD and image collection installation. Owing to the high spatial and spectral resolution, the system can operate in the spectral range from visible light to near infrared band. An experiment of detecting and recognizing of two different kinds of camouflage armets from background was presented. When the characteristic spectral wave bands are 680nm and 750nm, the two camouflage armets exhibit different spectral characteristic. The target camouflage armets in the hyperspectral images are distinct from background and the contrast of armets and background is increased. The image fusion, target segmentation and pick-up of those images with especial spectral characteristics were realized by the Hyperspectral Imaging System. The 600nm, 680nm, and 750nm images were processed by the Pseudo color fusion algorithm, thus the camouflage armets are more easily observed by naked eyes. Experimental results confirm that AOTF hyperspectral imaging system can acquire image of high contrast, and has the ability of detecting and identification camouflage objects.

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A fusion algorithm for infrared and visible images based on RDU-PCNN and ICA-bases in NSST domain

Infrared Physics & Technology ◽

10.1016/j.infrared.2016.10.015 ◽

2016 ◽

Vol 79 ◽

pp. 183-190 ◽

Cited By ~ 19

Author(s):

Zhanwen Liu ◽

Yan Feng ◽

Yifan Zhang ◽

Xu Li

Keyword(s):

Fusion Algorithm ◽

Visible Images

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1303 Simultaneous and Direct Observation of Near Infrared and Visible Images of Frictional Area During Scuffing

The Proceedings of the Machine Design and Tribology Division meeting in JSME ◽

10.1299/jsmemdt.2014.14.77 ◽

2014 ◽

Vol 2014.14 (0) ◽

pp. 77-80

Author(s):

Yosuke KAJI ◽

Kazuyuki YAGI ◽

Joichi SUGIMURA ◽

Seiji KAJITA ◽

Koji SAITO

Keyword(s):

Direct Observation ◽

Near Infrared ◽

Visible Images

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Fusion algorithm of infrared and visible images based on joint bilateral filter

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) ◽

10.1109/cisp-bmei.2017.8302023 ◽

2017 ◽

Cited By ~ 1

Author(s):

Hua Cai ◽

Guang-qiu Chen ◽

Zhi Liu ◽

Zhen-ye Geng

Keyword(s):

Bilateral Filter ◽

Fusion Algorithm ◽

Visible Images ◽

Joint Bilateral Filter

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Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

10.21203/rs.2.24788/v2 ◽

2020 ◽

Author(s):

Xiaoxue XING ◽

Cheng LIU ◽

Cong LUO ◽

Tingfa XU

Keyword(s):

Thermal Radiation ◽

High Frequency ◽

Geometric Analysis ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Algorithm ◽

Visible Image ◽

Fused Image ◽

Fusion Methods ◽

Visible Images

Abstract In Multi-scale Geometric Analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of the images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and Non-Subsampled Shearlet Transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the Wavelet Transform (WT) is used to decompose high-frequency sub-bands into obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

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Fusion algorithm of visible and infrared image based on anisotropic diffusion and image enhancement (capitalize only the first word in a title (or heading), the first word in a subtitle (or subheading), and any proper nouns)

PLoS ONE ◽

10.1371/journal.pone.0245563 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0245563

Author(s):

Hui Huang ◽

Linlu Dong ◽

Zhishuang Xue ◽

Xiaofang Liu ◽

Caijian Hua

Keyword(s):

Image Enhancement ◽

Anisotropic Diffusion ◽

Infrared Image ◽

Source Image ◽

Laplacian Pyramid ◽

Infrared Images ◽

Fusion Algorithm ◽

Public Data ◽

Visible Images ◽

Enhancement Algorithm

Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high.

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Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

10.21203/rs.2.24788/v1 ◽

2020 ◽

Author(s):

Xiaoxue XING ◽

Cheng LIU ◽

Cong LUO ◽

Tingfa XU

Keyword(s):

Thermal Radiation ◽

High Frequency ◽

Geometric Analysis ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Algorithm ◽

Visible Image ◽

Fused Image ◽

Fusion Methods ◽

Visible Images

Abstract In Multi-scale Geometric Analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of the images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and Non-Subsampled Shearlet Transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the wavelet transform(WT) is used to decompose high-frequency sub-bands into obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

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Synthetic aperture radar and color visible images fusion algorithm based on region division strategy

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.11.045012 ◽

2017 ◽

Vol 11 (04) ◽

pp. 1

Author(s):

Siyu Li ◽

Ling Wang

Keyword(s):

Synthetic Aperture Radar ◽

Synthetic Aperture ◽

Fusion Algorithm ◽

Region Division ◽

Visible Images ◽

Aperture Radar

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Novel Registration and Fusion Algorithm for Multimodal Railway Images with Different Field of Views

Journal of Advanced Transportation ◽

10.1155/2018/7836169 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Baoqing Guo ◽

Xingfang Zhou ◽

Yingzi Lin ◽

Liqiang Zhu ◽

Zujun Yu

Keyword(s):

Total Variation ◽

High Speed ◽

Low Frequency ◽

Local Region ◽

Contourlet Transform ◽

Fusion Algorithm ◽

Total Variation Model ◽

Fused Image ◽

Fusion Methods ◽

Visible Images

Objects intruding high-speed railway clearance do great threat to running trains. In order to improve accuracy of railway intrusion detection, an automatic multimodal registration and fusion algorithm for infrared and visible images with different field of views is presented. The ratio of the nearest to next nearest distance, geometric, similar triangle, and RANSAC constraints are used to refine the matching SURF feature points successively. Correct matching points are accumulated with multiframe to overcome the insufficient matching points in single image pair. After being registered, an improved Contourlet transform fusion algorithm combined with total variation and local region energy is proposed. Inverse Contourlet transform to low frequency subband coefficient fused with total variation model and high frequency subband coefficients fused with local region energy is used to reconstruct the fused image. The comparison to other 4 popular fusion methods shows that our algorithm has the best comprehensive performance for multimodal railway image fusion.

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