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e-ISSN 2383-1960
p-ISSN 2383-1979
Outline
Improving the quality of images synthesized by discrete cosine transform regression-based method using principle component analysis
Authors
1Radiation Research Center, Faculty of Paramedicine, AJA University of Medical Sciences, Tehran, Iran, Andorra
2Radiation Research Center, Faculty of Paramedicine, AJA University of Medical Sciences, Tehran, Iran, Iran
*Corresponding Author: Corresponding author: valiallah saba, Radiation Research Center, Faculty of Paramedicine, AJA University of Medical Sciences, Tehran, Iran, Iran Email: [email protected]
Published online:Jul 24, 2014
Article type:Research Article
Received:Mar 19, 2014
Accepted:May 27, 2014
How to Cite:Hamedani K, saba V. Improving the quality of images synthesized by discrete cosine transform regression-based method using principle component analysis. Ann Mil Health Sci Res. 2014;12(2):e63384. doi:
Abstract
Materials and Methods: Two new methods, based on neural networks and principle component analysis (PCA) were used to make virtual views of an image. The results were compared with those of the DCT-based method. Two distance metrics, i.e. mean square error (MSE) and structural similarity index measure (SSIM), were used to measure and compare image qualities. About 400 data were used to evaluate the performance of the new proposed methods.
Results: The neural networks fail to improve the quality of virtually produced images. However, principle component analysis improved the quality of the synthesized images about 3%.
Conclusion: Principle component analysis is better than both DCT-based and neural network methods for synthesizing virtual views of an image.
Highlights
References
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Copyright
© 2014, Annals of Military and Health Sciences Research. This open-access article is available under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) International License (https://creativecommons.org/licenses/by-nc/4.0/), which allows for the copying and redistribution of the material only for noncommercial purposes, provided that the original work is properly cited.
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