We introduce a new open-source toolkit for the well-tie or wavelet extraction problem of estimating seismic wavelets from seismic data, time-to-depth information, and well-log suites. The wavelet extraction model is formulated as a Bayesian inverse problem, and the software will simultaneously estimate wavelet coefficients, other parameters associated with uncertainty in the time-to-depth mappi...

High resolution multi-temporal, multi-sensor remotely sensed data are often used in Earth observation applications. Image fusion is a widely used method to integrate those data, while image registration and radiometric normalization are two necessary procedures in transforming multi-temporal or multi-sensor data into identical geometric and radiometric bases respectively. This Ph.D research att...

We develop a methodology for Bayesian hierarchical multi-subject multiscale analysis of functional Magnetic Resonance Imaging (fMRI) data. We begin by modeling the brain images temporally with a standard general linear model. After that, we transform the resulting estimated standardized regression coefficient maps through a discrete wavelet transformation to obtain a sparse representation in th...

financial companies are constantly exposed to the dangers of risk. in the last few years for various reasons, measuring value at risk (var) has become increasingly important for financial firms. the study of multiple measures of risk, var measure with a new approach provides the ground for calculation of market risk. common approaches to risk measurement due to complicated, nonlinear and changi...

Wavelet transforms and other multi-scale analysis functions have been used for compact signal and image representations in de-noising, compression and feature detection processing problems for about twenty years. Numerous research works have proven that space-frequency and spacescale expansions with this family of analysis functions provided a very efficient framework for signal or image data. ...

Image fusion deals with the integration of multi modal images from various sensors, with visual and infrared, to MMW images, aiming at achieving improved image quality to better support improved image classification, monitoring, surveillance and etc. The main goal of this paper is to introduce a new approach to fuse visual image and infrared images by complex wavelet taking depth of focus into ...

Signal classification has many important applications in both of the civilian and military domains. This paper presents a classification of multi-user chirp modulation signals using wavelet higher order statistics features and neural network classifier (NN). In this paper, even higher order moments and cumulants up to order eight from the discrete wavelet transform (DWT) coefficients are propos...

In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic for multi-channel image denoising. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This feature space distinguishes between important coefficients, which belong to image discontinuity and noisy coefficients. Besides this fuzzy feature, we use inter-relation betwee...

Wavelet families arise by scaling and translations of a prototype function, called the mother wavelet. The construction of wavelet bases for cardinal spline spaces is generally carried out within the multi-resolution analysis scheme. Thus, the usual way of increasing the dimension of the multi-resolution subspaces is by augmenting the scaling factor. We show here that, when working on a compact...

In this paper, a novel road extraction method using Stationary Wavelet Transform is proposed. To detect road features from color aerial satellite imagery, Mexican hat Wavelet filters are used by applying the Stationary Wavelet Transform in a multiresolution, multi-scale, sense and forming the products of Wavelet coefficients at a different scales to locate and identify road features at a few sc...