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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 4    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 4, April 2011 Edition
Slant Transformation As A Tool For Pre-processing In Image Processing
Full Text(PDF, 3000)  PP.  
Nagaraj B Patil, V M Viswanatha, Dr. Sanjay Pande MB
Discrete Wavelet transform,Compression ratio, Data Compresion, Peak-signal-to-ratio(PNSR), Coding Inter Pixel, Slantlet Coefficients, Choppy Images.
The Slantlet Transform (SLT) is a recently developed multiresolution technique especially well-suited for piecewise linear data. The Slantlet transform is an orthogonal Discrete Wavelet Transform (DWT) with 2 zero moments and with improved time localization. It also retains the basic characteristics of the usual filterbank such as octave band characteristic and a scale dilation factor of two. However, the Slantlet transform is based on the principle of designing different filters for different scales unlike iterated filterbank approaches for the DWT. In the proposed system, Slantlet transform is implemented and used in Compression and Denoising of various input images. The performance of Slantlet Transform in terms of Compression Ratio (CR), Reconstruction Ratio (RR) and Peak-Signal-to-Noise-Ratio (PSNR) present in the reconstructed images is evaluated. Simulation results are discussed to demonstrate the effectiveness of the proposed method.
1. I. W. Selesnick, “The Slantlet Transform,” IEEE Transaction on signal processing, vol 47, No. 5, May 1999.

2. David l. Donoho “Smooth wavelet decompositions with blocky coefficient kernels “ May, 1993.

3. Vaidyanathan, p. p., “Multirate systems and filter banks”, prentice hall inc., Englewood cliffs, new jersey, 1993.

4. M. Lmg, H. Guo, et. al “Noise reduction using an undecimated discrete wavelet transform”, IEEE signal processing , vol 3, no. 1, Jan 1996.

5. Aali M. Reza ,”From Fourier transform to Wavelet transform”, October 27, 1999 white paper.

6. Paolo Zatelli, Andrea Antonello, ” New Grass modules for multiresolution analysis with wavelets”, proceedings of the open source gis - grass users conference, September 2002.

7. Kenneth Lee, Sinhye Park, et.al, “Wavelet-based image and video compression”, Tcom 502, April, 1997.

8. Dr. Guangyi Chen , ”Signal or Image denoising using wavelets, multiwavelets and curvelets”, 2004.

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