Fast color-transfer-based image fusion method for merging infrared and visible images

2010 ◽  
Author(s):  
Guangxin Li ◽  
Shuyan Xu ◽  
Xin Zhao
Keyword(s):  
Image Fusion ◽  
Fusion Method ◽  
Color Transfer ◽  
Visible Images ◽  
Image Fusion Method

Polarization Image Fusion Method Using Color Transfer and Clustering-Based Segmentation

2011 ◽  
Vol 40 (1) ◽  
pp. 149-153 ◽  
Author(s):  
周浦城 ZHOU Pu-cheng ◽  
张洪坤 ZHANG Hong-kun ◽  
薛模根 XUE Mo-gen
Keyword(s):  
Image Fusion ◽  
Fusion Method ◽  
Color Transfer ◽  
Polarization Image ◽  
Image Fusion Method

Infrared and visible image fusion method based on rolling guidance filter and NSST

2019 ◽  
Vol 17 (06) ◽  
pp. 1950045 ◽  
Author(s):  
Cheng Zhao ◽  
Yongdong Huang
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Fusion Rule ◽  
Fusion Method ◽  
Visible Image ◽  
Guided Filtering ◽  
Gradient Domain ◽  
Visible Images ◽  
Image Fusion Method

The rolling guidance filtering (RGF) has a good characteristic which can smooth texture and preserve the edges, and non-subsampled shearlet transform (NSST) has the features of translation invariance and direction selection based on which a new infrared and visible image fusion method is proposed. Firstly, the rolling guidance filter is used to decompose infrared and visible images into the base and detail layers. Then, the NSST is utilized on the base layer to get the high-frequency coefficients and low-frequency coefficients. The fusion of low-frequency coefficients uses visual saliency map as a fusion rule, and the coefficients of the high-frequency subbands use gradient domain guided filtering (GDGF) and improved Laplacian sum to fuse coefficients. Finally, the fusion of the detail layers combines phase congruency and gradient domain guided filtering as the fusion rule. As a result, the proposed method can not only extract the infrared targets, but also fully preserves the background information of the visible images. Experimental results indicate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.

New optimised region-based multi-scale image fusion method for thermal and visible images

2014 ◽  
Vol 8 (5) ◽  
pp. 289-299 ◽  
Author(s):  
Veysel Aslantas ◽  
Rifat Kurban ◽  
Emre Bendes ◽  
Ahmet Nusret Toprak
Keyword(s):  
Image Fusion ◽  
Fusion Method ◽  
Multi Scale ◽  
Visible Images ◽  
Image Fusion Method

A false color image fusion method based on multi-resolution color transfer in normalization YCC space

Optik ◽  
2014 ◽  
Vol 125 (20) ◽  
pp. 6010-6016 ◽  
Author(s):  
Xuelian Yu ◽  
Jianle Ren ◽  
Qian Chen ◽  
Xiubao Sui
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Fusion Method ◽  
Color Transfer ◽  
False Color ◽  
False Color Image ◽  
Image Fusion Method

Multiwavelet-Based Image Fusion Method Using Unit-Linking Pulse Coupled Neural Networks

2014 ◽  
Vol 905 ◽  
pp. 548-551
Author(s):  
Xi Cai ◽  
Han Guang ◽  
Jin Kuan Wang
Keyword(s):  
Neural Networks ◽  
Image Fusion ◽  
Biological Activities ◽  
Fusion Method ◽  
Global Features ◽  
Coupled Neural Networks ◽  
Visible Images ◽  
Pulse Coupled Neural Networks ◽  
External Stimuli ◽  
Image Fusion Method

To simulate biological activities of human visual system to details and make full use of global features of source images, we propose a multiwavelet-based image fusion method using unit-linking pulse coupled neural networks (ULPCNNs) model. After motivated by external stimuli from images, ULPCNNs can produce series of binary pulses containing much global information. Then we employ the first firing time of each neuron as the salience measure. Experimental results demonstrate that, for multifocus images, remote sensing images, and infrared and visible images, our proposed method always generates satisfying fusion results.

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

Gradient-based multi-focus image fusion method using convolution neural network

2021 ◽  
Vol 92 ◽  
pp. 107174
Author(s):  
Yang Zhou ◽  
Xiaomin Yang ◽  
Rongzhu Zhang ◽  
Kai Liu ◽  
Marco Anisetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Convolution Neural Network ◽  
Fusion Method ◽  
Gradient Based ◽  
Focus Image ◽  
Image Fusion Method
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