A novel visible-infrared image fusion method based on visual enhancement and multiscale decomposition

A biologically inspired image fusion mechanism is analyzed in this paper. A pseudo-color image fusion method is proposed based on the improvement of a traditional method. The proposed model describes the fusion process using several abstract definitions which correspond to the detailed behaviors of neurons. Firstly, the infrared image and visible image are respectively ON against enhanced and OFF against enhanced. Secondly, we feed back the enhanced visible images given by the ON-antagonism system to the active cells in the center-surrounding antagonism receptive field. The fused [Formula: see text]VIS[Formula: see text]IR signal are obtained by feeding back the OFF-enhanced infrared image to the corresponding surrounding-depressing neurons. Then we feed back the enhanced visible signal from OFF-antagonism system to the depressing cells in the center-surrounding antagonism receptive field. The ON-enhanced infrared image is taken as the input signal of the corresponding active cells in the neurons, then the cell response of infrared-enhance-visible is produced in the process, it is denoted as [Formula: see text]IR[Formula: see text]VIS. The three kinds of signal are considered as R, G and B components in the output composite image. Finally, some experiments are performed in order to evaluate the performance of the proposed method. The information entropy, average gradient and objective image fusion measure are used to assess the performance of the proposed method objectively. Some traditional digital signal processing-based fusion methods are also evaluated for comparison in the experiments. In this paper, the Quantitative assessment indices show that the proposed fusion model is superior to the classical Waxman’s model, and some of its performance is better than the other image fusion methods.

Download Full-text

Target interpretation of visible light image and infrared image fusion method

AOPC 2017: Optical Sensing and Imaging Technology and Applications ◽

10.1117/12.2282251 ◽

2017 ◽

Author(s):

wei sun ◽

Wen Xiao ◽

Feng Pan ◽

Chao Du

Keyword(s):

Visible Light ◽

Image Fusion ◽

Infrared Image ◽

Fusion Method ◽

Light Image ◽

Image Fusion Method

Download Full-text

An Effective Image Fusion Method Based on Nonsubsampled Contourlet Transform and Pulse Coupled Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3542 ◽

2013 ◽

Vol 756-759 ◽

pp. 3542-3548 ◽

Cited By ~ 3

Author(s):

Li Juan Ma ◽

Chun Hui Zhao

Keyword(s):

Image Fusion ◽

Infrared Image ◽

Low Frequency ◽

Contourlet Transform ◽

Fusion Method ◽

Nonsubsampled Contourlet Transform ◽

Wavelet Method ◽

Weighted Sum Method ◽

Ihs Transform ◽

Image Fusion Method

In order to solve the problem of spectral distortion and the fuzzy texture in visible and infrared image fusion technology, a novel visible and infrared image fusion method based on the Nonsubsampled Contourlet Transform (NSCT) and Pulse Coupled Neural Networks (PCNN) is proposed in this paper. First, we gain three components of visible image, luminance I, chrominance H and saturation S, using the IHS transform. Then, we gain three coefficients, low frequency sub-band, passband sub-band and high frequency coefficient by decomposing the component I and infrared image with the help of the NSCT. Next, we use weighted-sum method to fuse the low frequency sub-band and PCNN method to fuse the other sub-band coefficient respectively. At last, we gain the fusion image by using the inverse IHS transform on the fusion component I gained by the inverse NSCT transform. Experiments show that our method have better fusion quality and can be more better to keep the visible spectral and detail information than some traditional methods such as, Laplace method, Wavelet method and Lifting Wavelet method.

Download Full-text