This paper investigates the statistical characterization of multiscale wavelet coefficients corresponding to random signals and images. Virtually all approaches to wavelet shrinkage model the wavelet coefficients as independent; we challenge that assumption and demonstrate several cases where substantial correlations may be present in the wavelet domain. In particular, the correlation between s...

generally, the presence of noise in geophysical measurements is inevitable and depending on the type and the level it affects the results of geophysical studies. so, denoising is an important part of the processing of geophysical data. on the other hand, geophysicists make inferences about the physical properties of the earth interior based on the indirect measurements (data) collected at or ne...

This paper presents a novel wavelet-based denoising and compression statistical model for 3D hippocampus shapes. Shapes are encoded using spherical wavelets and the objective is to remove noisy coefficients while keeping significant shape information. To do so, we develop a non-linear wavelet shrinkage model based on a data-driven Bayesian framework. We threshold wavelet coefficients by locally...

We propose a new wavelet-based method for image denoising that applies the Bayesian framework, using prior knowledge about the spatial clustering of the wavelet coefficients. Local spatial interactions of the wavelet coefficients are modeled by adopting a Markov Random Field model. An iterative updating technique known as iterated conditional modes (ICM) is applied to estimate the binary masks ...

Fractal image coding can be seen as spectral prediction of coefficients in the wavelet decomposition of an image. Fine scale coefficients are described by shifted and scaled coefficients from the next coarser frequency scale. We analyzed the ability of fractal coders to predict wavelet coefficients and found that the prediction of entire wavelet subtrees-as done by conventional spatial domain f...

It is well documented that natural images are compressible in wavelet bases and tend to exhibit fractal properties. In this paper, we investigate statistical models that mimic these behaviors. We then use our models to make predictions on the statistics of the wavelet coefficients. Following an innovation modeling approach, we identify a general class of finite-variance self-similar sparse rand...

we present a steganalysis scheme for lsb matching steganography based on feature vectors extracted from integer wavelet transform (iwt). in integer wavelet decomposition of an image, the coefficients will be integer, so we can calculate co-occurrence matrix of them without rounding the coefficients. before calculation of co-occurrence matrices, we clip some of the most significant bitplanes of ...