Infrared and visible image fusion based on QNSCT and Guided Filter

Optik ◽  
2022 ◽  
pp. 168592
Author(s):  
Chenxuan Yang ◽  
Yunan He ◽  
Ce Sun ◽  
Sheng Jiang ◽  
Ye Li ◽  
...  
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Visible Image

scholarly journals An Image Fusion Method Based on Curvelet Transform and Guided Filter Enhancement

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Zhang ◽  
Xu Ma ◽  
Yanshan Tian
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Curvelet Transform ◽  
Night Vision ◽  
Guided Filter ◽  
Source Image ◽  
Inverse Transformation ◽  
Fusion Method ◽  
Visible Image ◽  
Coupled Neural Networks

In order to improve the clarity of image fusion and solve the problem that the image fusion effect is affected by the illumination and weather of visible light, a fusion method of infrared and visible images for night-vision context enhancement is proposed. First, a guided filter is used to enhance the details of the visible image. Then, the enhanced visible and infrared images are decomposed by the curvelet transform. The improved sparse representation is used to fuse the low-frequency part, while the high-frequency part is fused with the parametric adaptation pulse-coupled neural networks. Finally, the fusion result is obtained by inverse transformation of the curvelet transform. The experimental results show that the proposed method has good performance in detail processing, edge protection, and source image information.

Infrared and visible image fusion using guided filter and convolutional sparse representation

2018 ◽  
Vol 26 (5) ◽  
pp. 1242-1253 ◽  
Author(s):  
刘先红 LIU Xian-hong ◽  
陈志斌 CHEN Zhi-bin ◽  
秦梦泽 QIN Meng-ze
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Guided Filter ◽  
Visible Image

Infrared and visible image fusion based on edge-preserving guided filter and infrared feature decomposition

2021 ◽  
Vol 186 ◽  
pp. 108108
Author(s):  
Long Ren ◽  
Zhibin Pan ◽  
Jianzhong Cao ◽  
Hui Zhang ◽  
Hao Wang
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Visible Image ◽  
Edge Preserving

Infrared and visible image fusion based on rolling guided filter and ResNet101

2021 ◽  
Author(s):  
Guangqiu Chen ◽  
Shuai Wang ◽  
Kaizhi Shang ◽  
Yucun Chen
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Visible Image

Infrared and visible image fusion algorithm based on three-layer guided filter and composition analysis CNN

2021 ◽  
Author(s):  
Chuanyun Wang ◽  
Guowei Yang ◽  
Dongdong Sun ◽  
Xiaoning Ma ◽  
Ziwei Li
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Composition Analysis ◽  
Fusion Algorithm ◽  
Visible Image

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.

Infrared and Visible Image Fusion Based on Deep Decomposition Network and Saliency Analysis

2021 ◽  
pp. 1-1
Author(s):  
Lihua Jian ◽  
Rakiba Rayhana ◽  
Ling Ma ◽  
Shaowu Wu ◽  
Zheng Liu ◽  
...  
Keyword(s):  
Image Fusion ◽  
Visible Image ◽  
Saliency Analysis

scholarly journals Infrared and visible image fusion via octave Gaussian pyramid framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Yan ◽  
Qun Hao ◽  
Jie Cao ◽  
Rizvi Saad ◽  
Kun Li ◽  
...  
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Visible Image ◽  
Composite Image ◽  
Multiscale Decomposition ◽  
Gaussian Pyramid ◽  
Fusion Methods ◽  
Scale Spaces ◽  
Fusion Framework

AbstractImage fusion integrates information from multiple images (of the same scene) to generate a (more informative) composite image suitable for human and computer vision perception. The method based on multiscale decomposition is one of the commonly fusion methods. In this study, a new fusion framework based on the octave Gaussian pyramid principle is proposed. In comparison with conventional multiscale decomposition, the proposed octave Gaussian pyramid framework retrieves more information by decomposing an image into two scale spaces (octave and interval spaces). Different from traditional multiscale decomposition with one set of detail and base layers, the proposed method decomposes an image into multiple sets of detail and base layers, and it efficiently retains high- and low-frequency information from the original image. The qualitative and quantitative comparison with five existing methods (on publicly available image databases) demonstrate that the proposed method has better visual effects and scores the highest in objective evaluation.

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