INTRODUCTION Wavelets are a mathematical tool for hierarchically decomposing functions in the frequency domain by preserving the spatial domain. Since their introduction [1], wavelets have found more and more applications in computer graphics, such as image compression, digital image processing, and feature detection [2-3]. Using wavelets, an image pyramid can be produced which represents the e...

In this paper we study the problem of template matching in signals, specifically focusing on images. First we present the solution to the case where the template is translated in the image, then discuss how to locate a template if it appears scaled in the image. The tools used for this scaling algorithm are wavelets, specifically the Haar wavelet. Wavelets naturally encode scaling information, ...

In this paper a new method of image data compression for medical images has been proposed to achieve high PSNR (Peak Signal to Noise Ratio). Image compression using Set Partitioning In Hierarchical Trees (SPIHT) transform is being compared with the other well known wavelets like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Dual Tree Complex Wavelet Transform (DTCWT) and Em...

NEW ALGORITHMS FOR NUMERICAL SOLUTION OF NONLINEAR INTEGRO-DIFFERENTIAL EQUATIONS OF THIRD ORDER USING HAAR WAVELETS M. Fayyaz a and M. Azram b * Department of Computer Science, CECOS University Peshawar, Pakistan. [email protected] b Faculty of Engineering, IIUM, Kuala Lumpur 50728, Malaysia. [email protected] ABSTRACT: This paper deals with the extension of earlier work [3] (designed for Fre...

Digital mammograms are coupled with noise which makes de-noising a challenging problem. In the literature, few wavelets like daubechies db3 and haar have been used for de-noising medical images. However, wavelet filters such as sym8, daubechies db4 and coif1 at certain level of soft and hard threshold have not been taken into account for mammogram images. Therefore, in this study five wavelet f...

In this paper a new multivariate regression estimate is introduced. It is based on ideas derived in the context of wavelet estimates and is constructed by hard thresholding of estimates of coefficients of a series expansion of the regression function. Multivariate functions constructed analogously to the classical Haar wavelets are used for the series expansion. These functions are orthogonal i...