ew 20(28): e12

Research Article

Hybrid Algorithm Based on Hyper Spectral Noise Removal for Satellite Image

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  • @ARTICLE{10.4108/eai.13-7-2018.163843,
    author={N. Hema Rajini},
    title={Hybrid Algorithm Based on Hyper Spectral Noise Removal for Satellite Image},
    journal={EAI Endorsed Transactions on Energy Web},
    volume={7},
    number={28},
    publisher={EAI},
    journal_a={EW},
    year={2020},
    month={3},
    keywords={Denoising, MSE, MSSIM, PSNR, SSI, Squared Blunder},
    doi={10.4108/eai.13-7-2018.163843}
}
  • N. Hema Rajini
    Year: 2020
    Hybrid Algorithm Based on Hyper Spectral Noise Removal for Satellite Image
    EW
    EAI
    DOI: 10.4108/eai.13-7-2018.163843
N. Hema Rajini1,*
  • 1: Associate Professor, Department of Computer Science and Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi – 630003, Tamil Nadu (State), India
*Contact email: auhemasmith@yahoo.co.in

Abstract

Usually the image obtained from a sensor is blurred by noise. Such noises are identified and eliminated by the procedure called denoising. The cross breed (Hybrid) algorithm is a popular technique that has been utilized recently. The Discrete Cosine Transformation (DCT) and Discrete Wave Transformation (DWT) are the generally utilized transform in which discrete cosine transformation requires less energy and computation resource and discrete wavelet transformation has multiple transformations. This Hybrid Algorithm combines the twin benefits of both the transformation and thus eliminates the negative contour and blocks the traces completely. The effectiveness of the proposed hybrid Algorithm is verified with different pictures by finding the average Squared Blunder, PSNR, Variance, architectural resemblance Index and average architectural resemblance Index.

Keywords
Denoising, MSE, MSSIM, PSNR, SSI, Squared Blunder
Received
2020-02-18
Accepted
2020-03-25
Published
2020-03-26
Publisher
EAI
http://dx.doi.org/10.4108/eai.13-7-2018.163843

Copyright © 2020 N. Hema Rajini et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.