Image Fusion using Eigen Features and Stationary Wavelet Transform
S. B. G. Tilak Babu1, K. H. K. Prasad2, Jyothirmai Gandeti3, Devi Bhavani Kadali4, V.Satyanarayana5, K. Pavani6
1S. B. G. Tilak Babu, Department of ECE, Aditya Engineering College, Surampalem, Andhra Pradesh, India.
2K. H. K. Prasad, Department of ECE, Aditya Engineering College, Surampalem, Andhra Pradesh, India.
3Jyothirmai Gandeti, Department of ECE, Aditya Engineering College, Surampalem, Andhra Pradesh, India.
4Devi Bhavani Kadali, Department of ECE, Aditya Engineering College, Surampalem, Andhra Pradesh, India.
5V. Satyanarayana, Department of ECE, Aditya Engineering College, Surampalem, Andhra Pradesh, India.
6K. Pavani, Department of ECE, Aditya Engineering College, Surampalem, Andhra Pradesh, India.
Manuscript received on 7 June 2019 | Revised Manuscript received on 12 June 2019 | Manuscript Published on 08 July 2019 | PP: 38-40 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10100688S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image fusion is a technique of fusing multiple images for better information and more accurate image compared source images. The applications of image fusion in modern military, multi-focus image integration, pattern recognition, remote sensing, biomedical imaging etc.In this paper discussed, pros and cons of various newly arrived existing techniques in spatial and transform domain image fusion techniques. The individual advantages of Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) is become great advantage to the proposed method.Standard dataset is used to evaluate the performance of proposed method, the obtained results are compared with exiting methodologies and shows robustness in terms of entropy, standard deviation and Peak Signal to Noise Ratio (PSNR).
Keywords: Fusion, multi-focus image integration, SWT, PCA, PSNR, standard deviation.
Scope of the Article: Image Processing and Pattern Recognition