4,916 research outputs found
Lidar waveform based analysis of depth images constructed using sparse single-photon data
This paper presents a new Bayesian model and algorithm used for depth and
intensity profiling using full waveforms from the time-correlated single photon
counting (TCSPC) measurement in the limit of very low photon counts. The model
proposed represents each Lidar waveform as a combination of a known impulse
response, weighted by the target intensity, and an unknown constant background,
corrupted by Poisson noise. Prior knowledge about the problem is embedded in a
hierarchical model that describes the dependence structure between the model
parameters and their constraints. In particular, a gamma Markov random field
(MRF) is used to model the joint distribution of the target intensity, and a
second MRF is used to model the distribution of the target depth, which are
both expected to exhibit significant spatial correlations. An adaptive Markov
chain Monte Carlo algorithm is then proposed to compute the Bayesian estimates
of interest and perform Bayesian inference. This algorithm is equipped with a
stochastic optimization adaptation mechanism that automatically adjusts the
parameters of the MRFs by maximum marginal likelihood estimation. Finally, the
benefits of the proposed methodology are demonstrated through a serie of
experiments using real data
XAFS spectroscopy. I. Extracting the fine structure from the absorption spectra
Three independent techniques are used to separate fine structure from the
absorption spectra, the background function in which is approximated by (i)
smoothing spline. We propose a new reliable criterion for determination of
smoothing parameter and the method for raising of stability with respect to
k_min variation; (ii) interpolation spline with the varied knots; (iii) the
line obtained from bayesian smoothing. This methods considers various prior
information and includes a natural way to determine the errors of XAFS
extraction. Particular attention has been given to the estimation of
uncertainties in XAFS data. Experimental noise is shown to be essentially
smaller than the errors of the background approximation, and it is the latter
that determines the variances of structural parameters in subsequent fitting.Comment: 16 pages, 7 figures, for freeware XAFS analysis program, see
http://www.crosswinds.net/~klmn/viper.htm
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Bayesian Robust Tensor Factorization for Incomplete Multiway Data
We propose a generative model for robust tensor factorization in the presence
of both missing data and outliers. The objective is to explicitly infer the
underlying low-CP-rank tensor capturing the global information and a sparse
tensor capturing the local information (also considered as outliers), thus
providing the robust predictive distribution over missing entries. The
low-CP-rank tensor is modeled by multilinear interactions between multiple
latent factors on which the column sparsity is enforced by a hierarchical
prior, while the sparse tensor is modeled by a hierarchical view of Student-
distribution that associates an individual hyperparameter with each element
independently. For model learning, we develop an efficient closed-form
variational inference under a fully Bayesian treatment, which can effectively
prevent the overfitting problem and scales linearly with data size. In contrast
to existing related works, our method can perform model selection automatically
and implicitly without need of tuning parameters. More specifically, it can
discover the groundtruth of CP rank and automatically adapt the sparsity
inducing priors to various types of outliers. In addition, the tradeoff between
the low-rank approximation and the sparse representation can be optimized in
the sense of maximum model evidence. The extensive experiments and comparisons
with many state-of-the-art algorithms on both synthetic and real-world datasets
demonstrate the superiorities of our method from several perspectives.Comment: in IEEE Transactions on Neural Networks and Learning Systems, 201
Detection of dirt impairments from archived film sequences : survey and evaluations
Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
Parallel Interference Cancellation Based Turbo Space-Time Equalization in the SDMA Uplink
A novel Parallel Interference Cancellation (PIC) based turbo Space Time Equalizer (STE) structure designed for multiple antenna assisted uplink receivers is introduced. The proposed receiver structure allows the employment of non-linear type of detectors such as the Bayesian Decision Feedback (DF) assisted turbo STE or the Maximum Aposteriori (MAP) STE, while operating at a moderate computational cost. Receivers based on the proposed structure outperform the linear turbo detector benchmarker based on the Minimum Mean-Squared Error (MMSE) criterion, even if the latter aims for jointly detecting all transmittersâ signals. Additionally the PIC based receiver is capable of equalizing non-linear binary pre-coded channels. The performance difference between the presented algorithms is discussed using Extrinsic Information Transferfunction (EXIT) charts. Index TermsâPIC, EXIT chart, precoding, Bayesian, STE
A Bayesian method for detecting stellar flares
We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares
in light curve data. We assume flares are described by a model in which there
is a rapid rise with a half-Gaussian profile, followed by an exponential decay.
Our signal model also contains a polynomial background model. This is required
to fit underlying light curve variations that are expected in the data, which
could otherwise partially mimic a flare. We characterise the false alarm
probability and efficiency of this method and compare it with a simpler
thresholding method based on that used in Walkowicz et al (2011). We find our
method has a significant increase in detection efficiency for low
signal-to-noise ratio (S/N) flares. For a conservative false alarm probability
our method can detect 95% of flares with S/N less than ~20, as compared to S/N
of ~25 for the simpler method. As an example we have applied our method to a
selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars
with a total of 1873 flares after vetos have been applied. For these flares we
have characterised their durations and and signal-to-noise ratios.Comment: Accepted for MNRAS. The code used for the analysis can be found at
https://github.com/BayesFlare/bayesflare/releases/tag/v1.0.
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