Denoising and Enhancement of Medical Images Using Wavelets in LabVIEW

Full Text (PDF, 950KB), PP.42-47

Views: 0 Downloads: 0


Yogesh Rao 1,* Nisha Sarwade 1 Roshan Makkar 2

1. VJTI, Mumbai, India

2. SAMEER, Mumbai, India

* Corresponding author.


Received: 21 May 2015 / Revised: 9 Jul. 2015 / Accepted: 28 Aug. 2015 / Published: 8 Oct. 2015

Index Terms

Contrast enhancement, Image Denoising, General histogram equalization, Resolution enhancement, SVD Theorem, Wavelets


In this paper, we have proposed a novel image enhancement technique based on M band wavelets. The conventional image enhancement algorithms opt for contrast enhancement using equalization techniques. Contrast enhancement is one of the most important issues in image enhancement techniques. High difference in luminance reflected from two adjacent surfaces results in a good contrast image which makes the object more distinguishable from other objects in the background. Many a times owing to over contrast, minute details of the images are lost; which cannot be tolerated for biomedical images. Moreover, they don't account for the noise embedded in the images. Also denoising using conventional filters result in blurring of images. The proposed algorithm not only denoises the image by retaining the high frequency edges, but also increases the contrast and generates a high resolution image. Various parameters like MSE and PSNR are been taken into account for comparison of enhanced images generated from the proposed algorithm with that of the conventional techniques.

Cite This Paper

Yogesh Rao, Nisha Sarwade, Roshan Makkar,"Denoising and Enhancement of Medical Images Using Wavelets in LabVIEW", IJIGSP, vol.7, no.11, pp.42-47, 2015. DOI: 10.5815/ijigsp.2015.11.06


[1]H. Ibrahim and N.S.P. Kong, "Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement", IEEE Transaction on Consumer Electronics, Vol. 53, No 4, Nov 2007, pp. 1752-1758. 

[2]H. Demirel, G. Anbarjafari, and M. N. S. Jahromi, "Image equalization based on singular value decomposition", Computer and Information Sciences, 2008. ISCIS '08. 23rd International Symposium DOI: 10.1109/ISCIS.2008.4717878, pg 1-5

[3]Hasan Demirel, G Anbarjafari, "Complex wavelet transform and singular wavelet decomposition based image transform", Signal Processing and Communications Applications Conference (SIU), 2010 IEEE, pg 332-335

[4]P Rasti, "Wavelet transform based new interpolation technique for satellite image resolution enhancement", ICARES 2014 IEEE Conference, pg 185-188

[5]R. C. Gonzalez, and R. E. Woods, Digital Image Processing, Prentice Hall, ISBN 013168728X, 2007. 

[6]Wen-Chun Shih "Time Frequency Analysis and Wavelet Transform Tutorial Wavelet for Music Signal Analysis"

[7]S. Venkata Ramana, S. Narayana Reddy, "A novel method to improve resolution of satellite images using DWT and interpolation", IJAREEIE, vol 3, issue 1, 2014. 

[8]Neha Tripathi, Krishna Gopal Kirar, "Image Resolution Enhancement by Wavelet Transform Based Interpolation and Image Fusion", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 8, August 2014.

[9]Cagri Ozcinar, Hasan Demirel and Gholamreza Anbarjafari, Image Equalization Using Singular Value Decomposition and Discrete Wavelet Transform,

[10]Pratt, W., 1991, Digital Image Processing, John Wiley and Sons, Inc.: Toronto.

[11]"The Wavelet Tutorial Second Edition", Robi Polikar, IIT Delhi

[12]Image processing toolbox help, MATLAB? [Online]. Available: