An Improved Image Denoising Approach Using Optimized Variance-Stabilizing Transformations

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Image denoising plays a very important process in image processing. Many researchers would study about the various noise removal approaches used for fluorescence images. The fluorescence images mainly contain Poisson noise. The process of denoising is the image is gaussianized first then the resulting image is given as an input to the OWT SURELET to remove the Gaussian white noise. The OWT SURELET is one of the conventional denoising algorithms used to remove the noises. Then to attain an exact signal an inverse transform is applied to the denoised signal. Difficulties may arise to choose the inverse transformation for fluorescence images because the bias error may occur if the non linear forward transform is applied.  To overcome these difficulties a study is made on the proposed Anscombe transformation and OWT-SURELET suitable for fluorescence images. The proposed OWT-SURELET is compared with BLS_GSM strategy and the results are discussed. Experimental result shows that the proposed system is more efficient than the existing system, the results are tested using ISNR changes with the denoising algorithms and the inverse transforms.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Clustering Analysis; Ontology; R&D; Text Mining; Knowledge Based Agent; Fuzzy SOM; NRGA Algorithm

Full Text:



K. Sampathkumar ,Dr.C. Arun, Poisson Noise Removal from Fluorescence Images Using Optimized Variance-Stabilizing Transformations and Standard Gaussian Denoising Strategies., European Journal of Scientific Research, ISSN 1450-216X Vol.84 No.3 pp.336-344, 2012.

Li, A., Wu, J., Modeling and prediction with wavelet neural network in the on-linear time series, (2012) International Review on Computers and Software (IRECOS), 7 (7), pp. 3617-3621.

Fryzlewicz, P., and G.P. Nason, .A Haar-Fisz Algorithm for Poisson Intensity Estimation., Journal of Computational and Graphical Statistics, vol. 13, no. 3, pp. 621.638, 2004.

Kolaczyk, E.D., and D.D. Dixon, .Nonparametric estimation of intensity maps using Haar wavelets and Poisson noise characteristics, The Astrophysical Journal, vol. 534, no. 1, pp. 490.505, 2000.

Willett, R.M., and R.D. Nowak, .Platelets: A Multiscale Approach for Recovering Edges and Surfaces in Photon-Limited Medical Imaging., IEEE Trans. Med. Imag., vol. 22, no. 3, pp. 332.350, March 2003.

Willett, R.M., .Multiscale Analysis of Photon-Limited Astronomical Images., Statistical Challenges in Modern Astronomy (SCMA) IV, 2006.

Anscombe, F.J., .The transformation of Poisson, binomial and negative binomial data., Biometrika, vol. 35, no. 3/4, pp. 246.254, Dec. 1948.

Portilla, J., V. Strela, M.J. Wainwright, and E.P. Simoncelli, .Image denoising using scale mixtures of Gaussians in the wavelet domain., IEEE Trans. Image Process., vol. 12, no. 11, pp. 1338.1351, Nov. 2003.

Lefkimmiatis, S., P. Maragos, and G. Papandreou, .Bayesian inference on multiscale models for Poisson intensity estimation: Applications to photon-limited image denoising., IEEE Trans. Image Process., vol. 18, no. 8, pp. 1724.1741, Aug. 2009

Thierry Blu, and Florian Luisier, The SURE-LET Approach to Image Denoising., IEEE Transactions On Image Processing, vol. 16, No. 11, November 2007.

Li, Q., Gray-level image threshold segmentation based on double set FCM and improved intra-class minimum, (2012) International Review on Computers and Software (IRECOS), 7 (4), pp. 1819-1824.

Lin, L., Ju, C., Zhou, W., Wang, X., Moving objects detection based on Gaussian mixture model, LBP texture and saliency map, (2012) International Review on Computers and Software (IRECOS), 7 (6), pp. 2921-2926.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2022 Praise Worthy Prize