wavelet domain) of unknown variance from SAR images. The method is based on the following procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the speckled image, 2) scaling and rounding to the coefficients of the highest subbands (to obtain integer and positive coefficients), 3) bit-slicing to the new highest subbands (to obtain bit-planes), 4) then we apply the Systholi...

We describe a novel method for removing noise (in wavelet domain) of unknown variance from microarrays. The method is based on the following procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the Noisy Microarray, 2) scaling and rounding to the coefficients of the highest subbands (to obtain integer and positive coefficients), 3) bit-slicing to the new highest subbands ...

Browsing and visualizing large datasets is often a tedious chore. Locating features, especially in a wavelet transform domain is usually offered as a possible solution. Wavelet transforms decorrelate data and facilitate progressive access through streaming. The work reported here describes a scheme that allows the user to first visualize regions containing significant features. Various region a...

This study presents an original method to detect edges in Synthetic Aperture Radar images corrupted by speckle. We show that the Continuous Wavelet Transform (CWT) allows to accurately detect edges embedded in speckle. Edges give rise to a conic signature in the CWT. Furthermore, the Signal t o Noise Ratio (SNR) increases with the scale of the CWT. The optimal wavelet maximizing the SNR is deri...

wavelet domain) of unknown variance from microarrays. The method is based on the following procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the Noisy Microarray, 2) scaling and rounding to the coefficients of the highest subbands (to obtain integer and positive coefficients), 3) bit-slicing to the new highest subbands (to obtain bit-planes), 4) then we apply the Systh...

In this paper, we present an edge detection method based on wavelet transform and Hessian matrix of image at each pixel. Many methods which based on wavelet transform, use wavelet transform to approximate the gradient of image and detect edges by searching the modulus maximum of gradient vectors. In our scheme, we use wavelet transform to approximate Hessian matrix of image at each pixel, too. ...

R. Mutihac Electricity and Biophysics, University of Bucharest, Bucharest, Romania Objective Analysis of an fMRI block-based visual stimulation paradigm was comparatively performed by wavelet analysis and statistical parametric mapping (SPM) [1] based on Gaussian Random Field Theory (RFT). The voxels were isotropic and the same general linear model (GLM) was employed in both SPM and the discret...

Introduction: One of the major problems in the development of nuclear medicine images is the presence of noise. The noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a Fourier transform environment. The main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times...

Copyright protection of digital content has become one of important issues in digital content marketplace. Digital watermarking may be used as an effective method for identifying the copyright ownership of digital content against unauthorized use and distribution. In this paper, we propose a robust digital image watermarking algorithm based on the multiple transform method, discrete wavelet tra...

A novel segmentation method based on wavelet transform is presented for gray matter, white matter and cerebrospinal fluid in thin-sliced single-channel brain magnetic resonance (MR) scans. On the basis of the local image model, multicontext wavelet-based thresholding segmentation (MCWT) is proposed to classify 2D MR data into tissues automatically. In MCWT, the wavelet multiscale transform of l...