Search arXiv⌕ Search

arXiv · math/0605623

The Hyperanalytic Wavelet Transform

Abstract

In this paper novel classes of 2-D vector-valued spatial domain wavelets are defined, and their properties given. The wavelets are 2-D generalizations of 1-D analytic wavelets, developed from the Generalized Cauchy-Riemann equations and represented as quaternionic functions. Higher dimensionality complicates the issue of analyticity, more than one `analytic' extension of a real function is possible, and an `analytic' analysis wavelet will not necessarily construct `analytic' decomposition coefficients. The decomposition of locally unidirectional and/or separable variation is investigated in detail, and two distinct families of hyperanalytic wavelet coefficients are introduced, the monogenic and the hypercomplex wavelet coefficients. The recasting of the analysis in a different frame of reference and its effect on the constructed coefficients is investigated, important issues for sampled transform coefficients. The magnitudes of the coefficients are shown to exhibit stability with respect to shifts in phase. Hyperanalytic 2-D wavelet coefficients enable the retrieval of a phase-and-magnitude description of an image in phase space, similarly to the description of a 1-D signal with the use of 1-D analytic wavelets, especially appropriate for oscillatory signals. Existing 2-D directional wavelet decompositions are related to the newly developed framework, and new classes of mother wavelets are introduced.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S. C. Olhede, G. Metikas. 2006-05-23. The Hyperanalytic Wavelet Transform. https://doi.org/10.1109/tsp.2009.2023397

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Adaptive Density Estimation Using Projection Kernels and Penalized Comparison to Overfitting

In this work, we study wavelet projection estimators for density estimation, based on compactly supported $\mathcal S$-regular scaling functions. The main issue is the choice of the resolution level, which determines the bias--variance trade-off. We select this level by a Penalized Comparison to Overfitting (PCO) criterion: each candidate estimator is compared in $Ł^2(\R)$ with a single overfitting reference, and the additional stochastic fluctuation is corrected by an explicit penalty. For the selected estimator, we prove a high-probability oracle inequality and a risk oracle inequality in expectation. Over Besov balls of densities satisfying a common $Ł^\infty$ bound, the risk inequality yields the rate $n^{-2r/(2r+1)}$ uniformly over the class, with a sufficient penalty threshold that can be chosen uniformly over the class. This rate has the classical minimax order, and the procedure adapts to the unknown Besov regularity. Numerical experiments on several density shapes confirm that the data-driven level is close to the oracle one and adapts to the structure of the target.

math.ST↗

Imputation is all you need: double robustness, semiparametric efficiency, and automatic covariate balance for estimating the average treatment effect

Imputation-based causal estimation is typically viewed as relying exclusively on an outcome model, in contrast to augmented inverse-probability weighting, whose consistency is protected by fitting two nuisance models. This paper argues that this view can be misleading by highlighting a hidden dual weighting structure in least-squares sieve regression imputation. Although only outcome regressions are explicitly fitted, the resulting imputation estimator admits an exact weighting representation whose induced weights balance every function in the sieve space and the corresponding population weighting functions are the $L^2$ projections of the inverse propensity scores onto the same sieve space. This projection structure yields an implicit form of double robustness and, under standard sieve approximation and growth conditions, asymptotic linearity with the efficient influence function. Thus, weighting, covariate balance, double robustness, and semiparametric efficiency can all emerge from imputation alone through the geometry of least-squares projection.

math.ST↗

High-accuracy simulation of Picard HMC, part I: Gaussian cloud correction

We study the problem of sampling from a continuous density $π\propto \exp(-V)$ on $\mathbb R^d$, where $V\in C^2(\mathbb R^d)$ has a $β$-Lipschitz gradient and $π$ satisfies a logarithmic Sobolev inequality with constant $α^{-1}$, and write $κ= β/α$. We introduce the Gaussian cloud sampler, which achieves total variation accuracy $\varepsilon$ using $\widetilde O(κd^{1/5}\,\text{polylog}(1/\varepsilon))$ gradient queries in expectation. The algorithm uses first-order rejection sampling (FORS) to correct the law of smoothed Picard HMC trajectories. To do so, we represent the iterates of the ideal Picard iteration via Gaussian clouds, whose centers are never evaluated, and we develop a suite of likelihood correction gadgets which only use samples from this indirect cloud representation.

math.ST↗