Infrared and visible image fusion based on region of interest detection and nonsubsampled contourlet transform

2013 ◽  
Vol 18 (5) ◽  
pp. 526-534 ◽  
Author(s):  
Huan-xi Liu ◽  
Tian-hong Zhu ◽  
Jia-jia Zhao
Keyword(s):  
Image Fusion ◽  
Region Of Interest ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Visible Image ◽  
Interest Detection

Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain

2017 ◽  
Vol 11 (01) ◽  
pp. 1 ◽  
Author(s):  
Kangjian He ◽  
Dongming Zhou ◽  
Xuejie Zhang ◽  
Rencan Nie ◽  
Quan Wang ◽  
...  
Keyword(s):  
Image Fusion ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Nonsubsampled Contourlet Transform ◽  
Visible Image ◽  
Target Extraction

scholarly journals Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Kangjian He ◽  
Dongming Zhou ◽  
Xuejie Zhang ◽  
Rencan Nie
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Weighted Average ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Background Region ◽  
Visible Image ◽  
Region Detection ◽  
Fused Image

The most fundamental purpose of infrared (IR) and visible (VI) image fusion is to integrate the useful information and produce a new image which has higher reliability and understandability for human or computer vision. In order to better preserve the interesting region and its corresponding detail information, a novel multiscale fusion scheme based on interesting region detection is proposed in this paper. Firstly, the MeanShift is used to detect the interesting region with the salient objects and the background region of IR and VI. Then the interesting regions are processed by the guided filter. Next, the nonsubsampled contourlet transform (NSCT) is used for background region decomposition of IR and VI to get a low-frequency and a series of high-frequency layers. An improved weighted average method based on per-pixel weighted average is used to fuse the low-frequency layer. The pulse-coupled neural network (PCNN) is used to fuse each high-frequency layer. Finally, the fused image is obtained by fusing the fused interesting region and the fused background region. Experimental results demonstrate that the proposed algorithm can integrate more background details as well as highlight the interesting region with the salient objects, which is superior to the conventional methods in objective quality evaluations and visual inspection.

A Nonsubsampled Contourlet Transform Based Medical Image Fusion Method

2013 ◽  
Vol 12 (4) ◽  
pp. 749-755 ◽  
Author(s):  
Shen Yu ◽  
Ren Enen ◽  
Dang Jian-Wu ◽  
Wang Guo-Hua ◽  
Feng Xin
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Medical Image Fusion ◽  
Image Fusion Method

Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform and Regional Fractal Dimension

2010 ◽  
Vol 39 (8) ◽  
pp. 1388-1393 ◽  
Author(s):  
刘少鹏 LIU Shao-peng ◽  
郝群 HAO Qun ◽  
宋勇 SONG Yong ◽  
胡摇 HU Yao
Keyword(s):  
Fractal Dimension ◽  
Image Fusion ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm
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