Self-Similar Vector Fields

We propose statistically self-similar and rotation-invariant models for vector fields, study some of the more significant properties of these models, and suggest algorithms and methods for reconstructing vector fields from numerical observations, using the same notions of self-similarity and invariance that give rise to our stochastic models. We illustrate the efficacy of the proposed schemes by applying them to the problems of denoising synthetic flow phantoms and enhancing flow-sensitive magnetic resonance imaging (MRI) of blood flow in the aorta. In constructing our models and devising our applied schemes and algorithms, we rely on two fundamental notions. The first of these, referred to as "innovation modelling" in the thesis, is the principle —applicable both analytically and synthetically— of reducing complex phenomena to combinations of simple independent components or "innovations". The second fundamental idea is that of "invariance", which indicates that in the absence of any distinguishing factor, two equally valid models or solutions should be given equal consideration

Fractional Brownian Models For Vector Field Data

In this note we introduce a vector generalization of fractional Brownian motion. Our definition takes into account directional properties of vector fields-such as divergence, rotational behaviour, and interactions with coordinate transformations-that have no counterpart in the scalar setting. Apart from the Hurst exponent which dictates the scale-dependent structure of the field, additional parameters of the new model control the balance between solenoidal and irrotational behaviour. This level of versatility makes these random fields potentially interesting candidates for the stochastic modelling of physical phenomena in various fields of application such as fluid dynamics, field theory, and medical image processing

Automatic Tonal Harmonization For Multi-Spectral Mosaics

When producing a mosaic of multiple multi-spectral images one needs to harmonize the colours so that the tone transition is smooth from one image to the other. Given two images Im(a) and Im(b), a transform T is sought to map Im(b) to an image that is harmonious in multi-spectral appearance to Im(a). We give the above problem of tonal harmonization an analytical framework, in which both ideal and practical solutions of the problem are studied. Using a physically motivated image formation model, we prove that a perfect tonal harmonizing operator cannot in general be found, but that whenever such an operator exists it is linear. In the latter case, finding the optimal harmonizing transformation can be cast as a linear programme (LP), which is a type of problem that can be efficiently solved using known techniques. Finally, strong empirical evidence is provided for the efficacy of the proposed solution

On Regularized Reconstruction of Vector Fields

In this paper, we give a general characterization of regularization functionals for vector field reconstruction, based on the requirement that the said functionals satisfy certain geometric invariance properties with respect to transformations of the coordinate system. In preparation for our general result, we also address some commonalities of invariant regularization in scalar and vector settings, and give a complete account of invariant regularization for scalar fields, before focusing on their main points of difference, which lead to a distinct class of regularization operators in the vector case. Finally, as an illustration of potential, we formulate and compare quadratic (L-2) and total-variation-type (L-1) regularized denoising of vector fields in the proposed framework

Stochastic Models for Sparse and Piecewise-Smooth Signals

Abstract—We introduce an extended family of continuous-domain stochastic models for sparse, piecewise-smooth signals. These are specified as solutions of stochastic differential equations, or, equivalently, in terms of a suitable innovation model; the latter is analogous conceptually to the classical interpretation of a Gaussian stationary process as filtered white noise. The two specific features of our approach are 1) signal generation is driven by a random stream of Dirac impulses (Poisson noise) instead of Gaussian white noise, and 2) the class of admissible whitening operators is considerably larger than what is allowed in the conventional theory of stationary processes. We provide a complete characterization of these finite-rate-of-innovation signals within Gelfand’s framework of generalized stochastic processes. We then focus on the class of scale-invariant whitening operators which correspond to unstable systems. We show that these can be solved by introducing proper boundary conditions, which leads to the specification of random, spline-type signals that are piecewise-smooth. These processes are the Poisson counterpart of fractional Brownian motion; they are nonstationary and have the same-type spectral signature. We prove that the generalized Poisson processes have a sparse representation in a wavelet-like basis subject to some mild matching condition. We also present a limit example of sparse process that yields a MAP signal estimator that is equivalent to the popular TV-denoising algorithm. Index Terms—Fractals, innovation models, Poisson processes, sparsity, splines, stochastic differential equations, stochastic processes