Wavelet transformation is one of the most practical mathematical transformations in the field of image processing, especially image and signal processing. Depending on the nature of the multiresolution analysis, Wavelet transformation become more accessible and powerful tools. In this paper, we refer to the mathematical foundations of this transformation.   Introduction: The...

in this paper some new applications of empirical mode decomposition (emd) and hilbert spectrum in seismic ground-roll attenuation, random noise attenuation and spectral decomposition are introduced. hilbert spectrum is a time-frequency representation for hilbert-huang transform which is obtained by combination of instantaneous frequency (if) concept and intrinsic mode function of empirical mode...

based on the compressed sensing theory, if a signal is sparse in a suitable space, by using the optimization methods, signal could be accurately reconstructed from measurements that are significantly less than the theoretical shannon requirements. the sparse representation may exist for the signal and it is not available for the noise; this could be used to distinguish these two. on the other h...

This paper presents a new way of representing a signal as well as an algorithm for extracting parameters of a speciic class of features. The signal representation is based on the observation that we are often able to associate features with operators transforming the signal. The novel results presented in this paper is derived by restricting the operators to continuous one-parametric orthogonal...

The topic of this report is signal representation in the context of hierarchical image processing. An overview of hierarchical processing systems is included as well as a presentation of various approaches to signal representation, feature representation and feature extraction. It is claimed that image hierarchies based on feature extraction, so called feature hierarchies, demand a signal repre...