A Real Time Multi-focus Image Fusion Framework

2014 ◽  
Vol 11 (4) ◽  
pp. 1295-1302
Author(s):  
Zhaojun Liu
Keyword(s):  
Image Fusion ◽  
Real Time ◽  
Focus Image ◽  
Fusion Framework

scholarly journals A Two-Stage Multi-Focus Image Fusion Framework Robust to Image Mis-Registration

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 123231-123243 ◽  
Author(s):  
Zhaoyang Ji ◽  
Xudong Kang ◽  
Kunzhong Zhang ◽  
Puhong Duan ◽  
Qiaobo Hao
Keyword(s):  
Image Fusion ◽  
Two Stage ◽  
Focus Image ◽  
Fusion Framework

A Multi-Focus Image Fusion Framework Based on Multi-Scale Sparse Representation in Gradient Domain

2021 ◽  
pp. 108254
Author(s):  
Yu Wang ◽  
Xiongfei Li ◽  
Rui Zhu ◽  
Zeyu Wang ◽  
Yuncong Feng ◽  
...  
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Multi Scale ◽  
Gradient Domain ◽  
Focus Image ◽  
Fusion Framework

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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