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DWT Based PCA and K-Means Clustering Block Level Approach for SAR Image De-Noising

D. Tripathi1 , V.K. Shukla2

Section:Research Paper, Product Type: Journal Paper
Volume-4 , Issue-12 , Page no. 87-91, Dec-2016

Online published on Jan 02, 2016

Copyright © D. Tripathi, V.K. Shukla . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: D. Tripathi, V.K. Shukla, “DWT Based PCA and K-Means Clustering Block Level Approach for SAR Image De-Noising,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.87-91, 2016.

MLA Style Citation: D. Tripathi, V.K. Shukla "DWT Based PCA and K-Means Clustering Block Level Approach for SAR Image De-Noising." International Journal of Computer Sciences and Engineering 4.12 (2016): 87-91.

APA Style Citation: D. Tripathi, V.K. Shukla, (2016). DWT Based PCA and K-Means Clustering Block Level Approach for SAR Image De-Noising. International Journal of Computer Sciences and Engineering, 4(12), 87-91.

BibTex Style Citation:
@article{Tripathi_2016,
author = {D. Tripathi, V.K. Shukla},
title = {DWT Based PCA and K-Means Clustering Block Level Approach for SAR Image De-Noising},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2016},
volume = {4},
Issue = {12},
month = {12},
year = {2016},
issn = {2347-2693},
pages = {87-91},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1138},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1138
TI - DWT Based PCA and K-Means Clustering Block Level Approach for SAR Image De-Noising
T2 - International Journal of Computer Sciences and Engineering
AU - D. Tripathi, V.K. Shukla
PY - 2016
DA - 2017/01/02
PB - IJCSE, Indore, INDIA
SP - 87-91
IS - 12
VL - 4
SN - 2347-2693
ER -

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Abstract

Visual data are transmitted as the high quality digital images in the major fields of communication in all of the modern applications. These images on receiving after transmission are most of the times corrupted with noise. This thesis focused on the work which works on the received image processing before it is used for particular applications. We applied image denoising which involves the manipulation of the DWT coefficients of noisy image data to produce a visually high standard denoised image. This works consist of extensive reviews of the various parametric and non parametric existing denoising algorithms based on statistical estimation approach related to wavelet transforms connected processing approach and contains analytical results of denoising under the effect of various noises at different intensities .These different noise models includes additive and multiplicative type�s distortions in images used. It includes Gaussian noise and speckle noise. The denoising algorithm is application independent and giving a very high speed performance with desired noise less image even in the presence of high level distortion. Hence, it is not required to have prior knowledge about the type of noise present in the image because of the adaptive nature of the proposed denoising algorithm.

Key-Words / Index Term

Image - denoising, DWT, Gaussian noise , PCA ,K mean clustering

References

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