Multi-Frame Image Fusion Method Combining Spatial-Temporal Saliency Detection and NSCT

Hai Peng; Hua Jun Feng; Ju Feng Zhao; Zhi Hai Xu; Qi Li; Yueting Chen

doi:10.4028/www.scientific.net/AMR.403-408.1927

Paper Titles

Performance of the Target Detection Based on Instantaneous Frequency Time-Frequency Energy Integration
p.1910

Region Growing Based Optical Fiber Panel Shadow Detection
p.1914

A Safety Assessment Method of Power Enterprises Based on Historical Case Knowledge
p.1918

The Design and Implementation of an Enterprise Mobile Information System Based on Internet of Things
p.1922

Multi-Frame Image Fusion Method Combining Spatial-Temporal Saliency Detection and NSCT
p.1927

Application Research of Kakadu System Based on JPEG2000
p.1933

An Anti-Noise Strategy of SAR Based on Compressive Sensing
p.1937

Design of an Energy Collection UWB Receiver Using Novel Synchronization and Detection Scheme
p.1941

A Novel Wearable Circularly Polarized Antenna in Body-Centric Wireless Communications
p.1946

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Multi-Frame Image Fusion Method Combining...

Multi-Frame Image Fusion Method Combining Spatial-Temporal Saliency Detection and NSCT

Abstract:

We propose a new image fusion method to fuse the frames of infrared and visual image sequences more effectively. In our method, we introduce an improved salient feature detection algorithm to achieve the saliency map of the original frames. This improved method can detect not only spatially but also temporally salient features using dynamic information of inter-frames. Images are then segmented into target regions and background regions based on saliency distribution. We formulate fusion rules for different regions using a double threshold method and finally fuse the image frames in NSCT multi-scale domain. Comparison of different methods shows that our result is a more effective one to stress salient features of target regions and maintain details of background regions from the original image sequences.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

1927-1932

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.1927

Citation:

Cite this paper

Online since:

November 2011

Authors:

Hai Peng, Hua Jun Feng, Ju Feng Zhao, Zhi Hai Xu, Qi Li, Yueting Chen

Keywords:

Double Threshold, Image Fusion, Image Process, Multi-Frame Images, NSCT, Spatial-Temporal Saliency Detection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zaveri T, Zaveri M. A novel region based image fusion method using highboost filtering [C]. IEEE, (2009).

DOI: 10.1109/icsmc.2009.5346076

Google Scholar

[2] Smith M I, Heather J P. A review of image fusion technology in 2005 [C]. (2005).

Google Scholar

[3] Zhao Peng, WANG Nihong, Pu Zhaobang. Image fusion based on morphological wavelet decomposition pyramid. Journal of Optoelctronics. Laser, 2008, 19 (6), 814-817.

Google Scholar

[4] Zhang Qiang, Guo Baolong. Remote Sensing Image Fusion Based on the Nonsubsampled Contourlet Transform [J]. ACTA OPTICA SINICA. 2008, 28(1): 74-80.

DOI: 10.3788/aos20082801.0074

Google Scholar

[5] LIU Gui-xi, ZHAO Shu-guang. Multi-sensor Image Fusion Scheme Based Oil Gradient Pyramid Decomposition. Journal of Optoelctronics. Laser, 2001, 12(3), 293-296.

Google Scholar

[6] Sasikala M, Kumaravel N. A comparative analysis of feature based image fusion methods [J]. Information Technology.

Google Scholar

[7] Da Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform: Theory, design, and applications [J]. Image Processing, IEEE Transactions on. 2006, 15(10): 3089-3101.

DOI: 10.1109/tip.2006.877507

Google Scholar

[8] Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection [J]. 2009, 1597-1604.

DOI: 10.1109/cvpr.2009.5206596

Google Scholar

[9] Information on http: /www. imagefusion. org.

Google Scholar