307 research outputs found
Highly Symmetric Multiple Bi-Frames for Curve and Surface Multiresolution Processing
Wavelets and wavelet frames are important and useful mathematical tools in numerous applications, such as signal and image processing, and numerical analysis. Recently, the theory of wavelet frames plays an essential role in signal processing, image processing, sampling theory, and harmonic analysis. However, multiwavelets and multiple frames are more flexible and have more freedom in their construction which can provide more desired properties than the scalar case, such as short compact support, orthogonality, high approximation order, and symmetry. These properties are useful in several applications, such as curve and surface noise-removing as studied in this dissertation. Thus, the study of multiwavelets and multiple frames construction has more advantages for many applications.
Recently, the construction of highly symmetric bi-frames for curve and surface multiresolution processing has been investigated. The 6-fold symmetric bi-frames, which lead to highly symmetric analysis and synthesis bi-frame algorithms, have been introduced. Moreover, these multiple bi-frame algorithms play an important role on curve and surface multiresolution processing. This dissertation is an extension of the study of construction of univariate biorthogonal wavelet frames (bi-frames for short) or dual wavelet frames with each framelet being symmetric in the scalar case. We will expand the study of biorthogonal wavelets and bi-frames construction from the scalar case to the vector case to construct biorthogonal multiwavelets and multiple bi-frames in one-dimension. In addition, we will extend the study of highly symmetric bi-frames for triangle surface multiresolution processing from the scalar case to the vector case.
More precisely, the objective of this research is to construct highly symmetric biorthogonal multiwavelets and multiple bi-frames in one and two dimensions for curve and surface multiresolution processing. It runs in parallel with the scalar case. We mainly present the methods of constructing biorthogonal multiwavelets and multiple bi-frames in both dimensions by using the idea of lifting scheme. On the whole, we discuss several topics include a brief introduction and discussion of multiwavelets theory, multiresolution analysis, scalar wavelet frames, multiple frames, and the lifting scheme. Then, we present and discuss some results of one-dimensional biorthogonal multiwavelets and multiple bi-frames for curve multiresolution processing with uniform symmetry: type I and type II along with biorthogonality, sum rule orders, vanishing moments, and uniform symmetry for both types. In addition, we present and discuss some results of two-dimensional biorthogonal multiwavelets and multiple bi-frames and the multiresolution algorithms for surface multiresolution processing. Finally, we show experimental results on curve and surface noise-removing by applying our multiple bi-frame algorithms
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representations in
image processing and computer vision challenging. The latter observation has
not prevented the design of image representations, which trade off between
efficiency and complexity, while achieving accurate rendering of smooth regions
as well as reproducing faithful contours and textures. The most recent ones,
proposed in the past decade, share an hybrid heritage highlighting the
multiscale and oriented nature of edges and patterns in images. This paper
presents a panorama of the aforementioned literature on decompositions in
multiscale, multi-orientation bases or dictionaries. They typically exhibit
redundancy to improve sparsity in the transformed domain and sometimes its
invariance with respect to simple geometric deformations (translation,
rotation). Oriented multiscale dictionaries extend traditional wavelet
processing and may offer rotation invariance. Highly redundant dictionaries
require specific algorithms to simplify the search for an efficient (sparse)
representation. We also discuss the extension of multiscale geometric
decompositions to non-Euclidean domains such as the sphere or arbitrary meshed
surfaces. The etymology of panorama suggests an overview, based on a choice of
partially overlapping "pictures". We hope that this paper will contribute to
the appreciation and apprehension of a stream of current research directions in
image understanding.Comment: 65 pages, 33 figures, 303 reference
Symmetric interpolatory dual wavelet frames
For any symmetry group and any appropriate matrix dilation we give an
explicit method for the construction of -symmetric refinable interpolatory
refinable masks which satisfy sum rule of arbitrary order . For each such
mask we give an explicit technique for the construction of dual wavelet frames
such that the corresponding wavelet masks are mutually symmetric and have the
vanishing moments up to the order n. For an abelian symmetry group we
modify the technique such that each constructed wavelet mask is -symmetric.Comment: 22 page
Sparse Representation on Graphs by Tight Wavelet Frames and Applications
In this paper, we introduce a new (constructive) characterization of tight
wavelet frames on non-flat domains in both continuum setting, i.e. on
manifolds, and discrete setting, i.e. on graphs; discuss how fast tight wavelet
frame transforms can be computed and how they can be effectively used to
process graph data. We start with defining the quasi-affine systems on a given
manifold \cM that is formed by generalized dilations and shifts of a finite
collection of wavelet functions .
We further require that is generated by some refinable function
with mask . We present the condition needed for the masks so that the associated quasi-affine system generated by is a
tight frame for L_2(\cM). Then, we discuss how the transition from the
continuum (manifolds) to the discrete setting (graphs) can be naturally done.
In order for the proposed discrete tight wavelet frame transforms to be useful
in applications, we show how the transforms can be computed efficiently and
accurately by proposing the fast tight wavelet frame transforms for graph data
(WFTG). Finally, we consider two specific applications of the proposed WFTG:
graph data denoising and semi-supervised clustering. Utilizing the sparse
representation provided by the WFTG, we propose -norm based
optimization models on graphs for denoising and semi-supervised clustering. On
one hand, our numerical results show significant advantage of the WFTG over the
spectral graph wavelet transform (SGWT) by [1] for both applications. On the
other hand, numerical experiments on two real data sets show that the proposed
semi-supervised clustering model using the WFTG is overall competitive with the
state-of-the-art methods developed in the literature of high-dimensional data
classification, and is superior to some of these methods
Multiscale Adaptive Representation of Signals: I. The Basic Framework
We introduce a framework for designing multi-scale, adaptive, shift-invariant
frames and bi-frames for representing signals. The new framework, called
AdaFrame, improves over dictionary learning-based techniques in terms of
computational efficiency at inference time. It improves classical multi-scale
basis such as wavelet frames in terms of coding efficiency. It provides an
attractive alternative to dictionary learning-based techniques for low level
signal processing tasks, such as compression and denoising, as well as high
level tasks, such as feature extraction for object recognition. Connections
with deep convolutional networks are also discussed. In particular, the
proposed framework reveals a drawback in the commonly used approach for
visualizing the activations of the intermediate layers in convolutional
networks, and suggests a natural alternative
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