A Novel Approach for High Intention Image with Gradient Pyramid
||International Journal of P2P Network Trends and Technology (IJPTT)||
|© 2018 by IJPTT Journal|
|Year of Publication : 2018|
|Authors : Bambang Chang|
MLA Style: Bambang Chang "A Novel Approach for High Intention Image with Gradient Pyramid" International Journal of P2P Network Trends and Technology 8.3 (2018): 1-4.
APA Style:Bambang Chang, (2018). A Novel Approach for High Intention Image with Gradient Pyramid. International Journal of P2P Network Trends and Technology, 8(3), 1-4.
The quality of an image directly related with its pictorial transparency, intense and contrast, in image fusion method it is an organization of perfect message from 2 or more input image into one single illustration such that the merged or output reviewed picture clutches entire of the information from original images. Here the two categories of image fusion algorithms are executed, pyramid based algorithm. Virtual reality outcomes protest that gradient pyramid progression is effective to multi-focus representation and color picture. The effective fusion images assorted from different equipment is great important in many applications such as curative imaging, microscopic imaging, remote sensing. In this report, we relate this element competently divergence enrichment structure for images that improves the eminence of detectable image without presenting improbable visual exteriors.
 Gonzalo Pajares , Jesus Manuel de la Cruz, “A wavelet-based image fusion tutorial” PatternRecognition 37 (2004) 1855 – 1872.
 H.B. Mitchell “Image Fusion Theories, Techniques and Applications”.
 YufengZheng “Image Fusion and Its Applications”.
 Hamid Reza Shahdoosti, Hassan Ghassemian, “Spatial PCA as A New Method for Image Fusion”,The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), 2012.
 Swathy Nair1, Bindu Elias2 and VPS Naidu, “ Pixel level image fusion using fuzzylet fusion algorithm” IJAREEIE An ISO 3297: 2007 Certified Organization, Vol. 2, SpecialIssue1, December2013.
 Er. Navjot Kaur, Er. Yadwinder Kaur,” Object classification Techniques using Machine Learning Model” International Journal of Computer Trends and Technology (IJCTT), Volume-18 Number-4, 2014
 Er. Simranpreet Singh, Er. Palak Sharma,” Image Fusion,” International Journal of advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 4, Issue 3,Mar. 2014.
 JianbingShen, Ying Zhao,Shuicheng Yen and Xuelong Li, “Exposure Fusion Using Boosting Laplacian Pyramid”, IEEE Transaction on Cybernetics, Vol. 44, No. 9, Sep. 2014.
 S Yasaswini, G.M.Naik, P G K Sirisha,”Efficient Loss Recovery in Ad Hoc Networks” International Journal ofComputer Science and Engineering (SSRG-IJCSE), Volume-4 Issue-1, 2017
 Deepali Sale, VarshaPatil, Dr. MadhuriA.Joshi, “ Effective Image Enhancement using Hybrid Multi- resolution Image Fusion”, IEEE global Conference on Wireless Computing and Networking (GCWCN), 2014.
 DeepuRajan and SubhasisChaudhuri, Generalized Interpolation and Its Application in Super-Resolution Imaging, Image and Vision Computing, Volume 19, Issue 13, , Pages 957-969, 1November 2001
 G. Simone, A. Farina, F. C. Morabito, S. B. Serpico and L. Bruzzone, “Image Fusion Techniques for Remote Sensing Applications”, Information Fusion, Volume 3, Issue 1, Pages 3-15, March 2002
 Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity”, IEEE Transactions on Image Processing, vol. 13, no. 4, pp.600-612, Apr. 2004.
 Yang C., Zhang J., Wang X., Liu X, "A novel similarity based quality metric for image fusion". Information Fusion 9(2): 156-160, 2008. 122.
 R. C. Luoand M. G. Kay, “Multisensor Integration and Fusion for Intelligent Machines and Systems” Norwood, NJ: Ablex, 1995.
 ZhiyunXue, “Image Fusion”, PhD Thesis, University of Lehigh, USA. August 2006.
 G. Piella. A general framework for multiresolution image fusion: From pixels to regions [J]. Information Fusion, 2003, 4(4):259-280
 L. Yiyao, Y. V. Venkatesh, C. C. Ko. A knowledge-based neural network for fusing edge maps of multi-sensor images [J]. Information Fusion, 2001, 2(2):121-133
 A. H. Gunatilaka, B. A. Baertlein. Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2001, 23(6):577-589.
 A. S. Solberg, T. Tact, A. K. Jain. A markov“random field model for classification of multisource satellite imagery”IEEETrans.On Geoscience and Remote Sensing. 1996, 34(1):100-113.
 Meenakshi Sharma, Vishal chowdhary, Alka thakur,”A Novel Hybrid Technique Based upon Static and Dynamic Clustering to Reduce Consumption in Wireless Sensor Network” International Journal of Computer & Organization Trends (IJCOT),Volume - 6 Issue – 2, 2016
 B. Jeon, D. A. Landgrebe. “Decision fusion approach for multitemporal classification” [J]. IEEE Trans. on Geoscience and Remote Sensing, 1999, 37(3):1227-1233.
 Y. Xia, H. Leung, E. Bosse. “Neural data fusion algorithm based on a linearly constrained least square method”,IEEE Trans. on Neural Networks. 2002, 13(2):320-329.
 J. M. Laferte, F. Heitz. Hierarchical,“statistical model for the fusion of multisensor image data”,in Proceedings of the International Conference on Computer Vision. Cambridge, June 1995, 908-913.
 Peter J Burt, Edward Adelson, “Laplacian Pyramid as a Compact Image Code”, IEEE Transactions on Communications, Vol Com-31, No. 4, April 1983.
 Zhang Zhong, ``Investigations on Image Fusion,``, PhD Thesis, University of Lehigh, USA. May 1999.
 D. L. Hall and J. Llinas, “An introduction to multisensor data fusion”, Proc. IEEE, vol. 85, pp. 6–23, Jan. 1997.
 Jinzhong Yang, “Image Fusion”, PhD Thesis, University of Lehigh, USA. August 2006.
gradient pyramid, image fusion, entropy, multi-scale, multi-resolution.