430 research outputs found
Multitaper estimation on arbitrary domains
Multitaper estimators have enjoyed significant success in estimating spectral
densities from finite samples using as tapers Slepian functions defined on the
acquisition domain. Unfortunately, the numerical calculation of these Slepian
tapers is only tractable for certain symmetric domains, such as rectangles or
disks. In addition, no performance bounds are currently available for the mean
squared error of the spectral density estimate. This situation is inadequate
for applications such as cryo-electron microscopy, where noise models must be
estimated from irregular domains with small sample sizes. We show that the
multitaper estimator only depends on the linear space spanned by the tapers. As
a result, Slepian tapers may be replaced by proxy tapers spanning the same
subspace (validating the common practice of using partially converged solutions
to the Slepian eigenproblem as tapers). These proxies may consequently be
calculated using standard numerical algorithms for block diagonalization. We
also prove a set of performance bounds for multitaper estimators on arbitrary
domains. The method is demonstrated on synthetic and experimental datasets from
cryo-electron microscopy, where it reduces mean squared error by a factor of
two or more compared to traditional methods.Comment: 28 pages, 11 figure
Image Reconstruction from Undersampled Confocal Microscopy Data using Multiresolution Based Maximum Entropy Regularization
We consider the problem of reconstructing 2D images from randomly
under-sampled confocal microscopy samples. The well known and widely celebrated
total variation regularization, which is the L1 norm of derivatives, turns out
to be unsuitable for this problem; it is unable to handle both noise and
under-sampling together. This issue is linked with the notion of phase
transition phenomenon observed in compressive sensing research, which is
essentially the break-down of total variation methods, when sampling density
gets lower than certain threshold. The severity of this breakdown is determined
by the so-called mutual incoherence between the derivative operators and
measurement operator. In our problem, the mutual incoherence is low, and hence
the total variation regularization gives serious artifacts in the presence of
noise even when the sampling density is not very low. There has been very few
attempts in developing regularization methods that perform better than total
variation regularization for this problem. We develop a multi-resolution based
regularization method that is adaptive to image structure. In our approach, the
desired reconstruction is formulated as a series of coarse-to-fine
multi-resolution reconstructions; for reconstruction at each level, the
regularization is constructed to be adaptive to the image structure, where the
information for adaption is obtained from the reconstruction obtained at
coarser resolution level. This adaptation is achieved by using maximum entropy
principle, where the required adaptive regularization is determined as the
maximizer of entropy subject to the information extracted from the coarse
reconstruction as constraints. We demonstrate the superiority of the proposed
regularization method over existing ones using several reconstruction examples
Tensor Approximation for Multidimensional and Multivariate Data
Tensor decomposition methods and multilinear algebra are powerful tools to cope with challenges around multidimensional and multivariate data in computer graphics, image processing and data visualization, in particular with respect to compact representation and processing of increasingly large-scale data sets. Initially proposed as an extension of the concept of matrix rank for 3 and more dimensions, tensor decomposition methods have found applications in a remarkably wide range of disciplines. We briefly review the main concepts of tensor decompositions and their application to multidimensional visual data. Furthermore, we will include a first outlook on porting these techniques to multivariate data such as vector and tensor fields
Hybrid Functional-Neural Approach for Surface Reconstruction
ABSTRACT. This paper introduces a new hybrid functional-neural approach for surface reconstruction. Our approach is based on the combination of two powerful artificial intelligence paradigms: on one hand, we apply the popular Kohonen neural network to address the data parameterization problem. On the other hand, we introduce a new functional network, called NURBS functional network, whose topology is aimed at reproducing faithfully the functional structure of the NURBS surfaces. These neural and functional networks are applied in an iterative fashion for further surface refinement. The hybridization of these two networks provides us with a powerful computational approach to obtain a NURBS fitting surface to a set of irregularly sampled noisy data points within a prescribed error threshold. The method has been applied to two illustrative examples. The experimental results confirm the good performance of our approach.This research has been kindly supported by the Computer
Science National Program of the Spanish Ministry of Economy
and Competitiveness, Project ref. no. TIN2012-30768,
Toho University (Funabashi, Japan), and the University of
Cantabria (Santander, Spain)
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