18,260 research outputs found
Sub-Pixel Registration of Wavelet-Encoded Images
Sub-pixel registration is a crucial step for applications such as
super-resolution in remote sensing, motion compensation in magnetic resonance
imaging, and non-destructive testing in manufacturing, to name a few. Recently,
these technologies have been trending towards wavelet encoded imaging and
sparse/compressive sensing. The former plays a crucial role in reducing imaging
artifacts, while the latter significantly increases the acquisition speed. In
view of these new emerging needs for applications of wavelet encoded imaging,
we propose a sub-pixel registration method that can achieve direct wavelet
domain registration from a sparse set of coefficients. We make the following
contributions: (i) We devise a method of decoupling scale, rotation, and
translation parameters in the Haar wavelet domain, (ii) We derive explicit
mathematical expressions that define in-band sub-pixel registration in terms of
wavelet coefficients, (iii) Using the derived expressions, we propose an
approach to achieve in-band subpixel registration, avoiding back and forth
transformations. (iv) Our solution remains highly accurate even when a sparse
set of coefficients are used, which is due to localization of signals in a
sparse set of wavelet coefficients. We demonstrate the accuracy of our method,
and show that it outperforms the state-of-the-art on simulated and real data,
even when the data is sparse
Non-Linear Phase-Shifting of Haar Wavelets for Run-Time All-Frequency Lighting
This paper focuses on real-time all-frequency image-based rendering using an
innovative solution for run-time computation of light transport. The approach
is based on new results derived for non-linear phase shifting in the Haar
wavelet domain. Although image-based methods for real-time rendering of dynamic
glossy objects have been proposed, they do not truly scale to all possible
frequencies and high sampling rates without trading storage, glossiness, or
computational time, while varying both lighting and viewpoint. This is due to
the fact that current approaches are limited to precomputed radiance transfer
(PRT), which is prohibitively expensive in terms of memory requirements and
real-time rendering when both varying light and viewpoint changes are required
together with high sampling rates for high frequency lighting of glossy
material. On the other hand, current methods cannot handle object rotation,
which is one of the paramount issues for all PRT methods using wavelets. This
latter problem arises because the precomputed data are defined in a global
coordinate system and encoded in the wavelet domain, while the object is
rotated in a local coordinate system. At the root of all the above problems is
the lack of efficient run-time solution to the nontrivial problem of rotating
wavelets (a non-linear phase-shift), which we solve in this paper
Estimation of Physiological Motion Using Highly Accelerated Continuous 2D MRI
Patient motion is well-known for degrading image quality during medical
imaging. Especially positron emission tomography (PET) is susceptible to motion
due to its usually long scan times. In hybrid PET/MRI (magnetic resonance
imaging), simultaneously acquired dynamic MRI data can be used to correct for
motion. Usually, MRI model-based motion correction approaches are applied to
the PET data. However, these approaches may fail for non-predictable, irregular
motion. We propose a novel approach for the continuous and real-time tracking
of motion using highly accelerated, dynamic MRI for an accurate motion
estimation. For this purpose, a TurboFLASH sequence is utilized in single-shot
mode with additional exploiting GRAPPA acceleration. Sampling frequency for one
slice is up to 26 ms and 520 ms for one 3D volume of 20 coronal slices.
Principal component analysis and a phase-sensitive resorting of slices is
performed to restore temporal consistency of the volumes. Motion is estimated
from these volumes using hyper-elastic registration. The approach is validated
with the help of a dynamic thorax phantom as well as with eleven healthy
volunteers. Phantom ground-truth data demonstrates that the approach produces
an accurate motion estimation. Volunteer validation proves that the approach is
also valid for different respiratory amplitudes including highly irregular
breathing. The approach could be proved to be promising for a continuous PET
motion correction
SPARCOM: Sparsity Based Super-Resolution Correlation Microscopy
In traditional optical imaging systems, the spatial resolution is limited by
the physics of diffraction, which acts as a low-pass filter. The information on
sub-wavelength features is carried by evanescent waves, never reaching the
camera, thereby posing a hard limit on resolution: the so-called diffraction
limit. Modern microscopic methods enable super-resolution, by employing
florescence techniques. State-of-the-art localization based fluorescence
subwavelength imaging techniques such as PALM and STORM achieve sub-diffraction
spatial resolution of several tens of nano-meters. However, they require tens
of thousands of exposures, which limits their temporal resolution. We have
recently proposed SPARCOM (sparsity based super-resolution correlation
microscopy), which exploits the sparse nature of the fluorophores distribution,
alongside a statistical prior of uncorrelated emissions, and showed that
SPARCOM achieves spatial resolution comparable to PALM/STORM, while capturing
the data hundreds of times faster. Here, we provide a detailed mathematical
formulation of SPARCOM, which in turn leads to an efficient numerical
implementation, suitable for large-scale problems. We further extend our method
to a general framework for sparsity based super-resolution imaging, in which
sparsity can be assumed in other domains such as wavelet or discrete-cosine,
leading to improved reconstructions in a variety of physical settings.Comment: 31 page
An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions
In this paper, we show that different body parts do not play equally
important roles in recognizing a human action in video data. We investigate to
what extent a body part plays a role in recognition of different actions and
hence propose a generic method of assigning weights to different body points.
The approach is inspired by the strong evidence in the applied perception
community that humans perform recognition in a foveated manner, that is they
recognize events or objects by only focusing on visually significant aspects.
An important contribution of our method is that the computation of the weights
assigned to body parts is invariant to viewing directions and camera parameters
in the input data. We have performed extensive experiments to validate the
proposed approach and demonstrate its significance. In particular, results show
that considerable improvement in performance is gained by taking into account
the relative importance of different body parts as defined by our approach.Comment: arXiv admin note: substantial text overlap with arXiv:1705.04641,
arXiv:1705.05741, arXiv:1705.0443
A Unified Framework for Multi-Sensor HDR Video Reconstruction
One of the most successful approaches to modern high quality HDR-video
capture is to use camera setups with multiple sensors imaging the scene through
a common optical system. However, such systems pose several challenges for HDR
reconstruction algorithms. Previous reconstruction techniques have considered
debayering, denoising, resampling (align- ment) and exposure fusion as separate
problems. In contrast, in this paper we present a unifying approach, performing
HDR assembly directly from raw sensor data. Our framework includes a camera
noise model adapted to HDR video and an algorithm for spatially adaptive HDR
reconstruction based on fitting of local polynomial approximations to observed
sensor data. The method is easy to implement and allows reconstruction to an
arbitrary resolution and output mapping. We present an implementation in CUDA
and show real-time performance for an experimental 4 Mpixel multi-sensor HDR
video system. We further show that our algorithm has clear advantages over
existing methods, both in terms of flexibility and reconstruction quality
Volumetric Super-Resolution of Multispectral Data
Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either the information in MS and PAN images are fused (i.e.
pansharpening) or super-resolution reconstruction (SRR) is used with only MS
images captured on different dates. Existing methods do not utilize temporal
information of MS and high spatial resolution of PAN images together to improve
the resolution. In this paper, we propose a multiframe SRR algorithm using
pansharpened MS images, taking advantage of both temporal and spatial
information available in multispectral imagery, in order to exceed spatial
resolution of given PAN images. We first apply pansharpening to a set of
multispectral images and their corresponding PAN images captured on different
dates. Then, we use the pansharpened multispectral images as input to the
proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The
proposed SRR method is obtained by deriving the subband relations between
multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images
comparing our method to conventional techniques.Comment: arXiv admin note: text overlap with arXiv:1705.0125
A high-order wideband direct solver for electromagnetic scattering from bodies of revolution
The generalized Debye source representation of time-harmonic electromagnetic
fields yields well-conditioned second-kind integral equations for a variety of
boundary value problems, including the problems of scattering from perfect
electric conductors and dielectric bodies. Furthermore, these representations,
and resulting integral equations, are fully stable in the static limit as
in multiply connected geometries. In this paper, we present the
first high-order accurate solver based on this representation for bodies of
revolution. The resulting solver uses a Nystr\"om discretization of a
one-dimensional generating curve and high-order integral equation methods for
applying and inverting surface differentials. The accuracy and speed of the
solvers are demonstrated in several numerical examples
Splines are Universal Solutions of Linear Inverse Problems with Generalized-TV regularization
Splines come in a variety of flavors that can be characterized in terms of
some differential operator L. The simplest piecewise-constant model corresponds
to the derivative operator. Likewise, one can extend the traditional notion of
total variation by considering more general operators than the derivative. This
leads us to the definition of the generalized Beppo-Levi space M, which is
further identified as the direct sum of two Banach spaces. We then prove that
the minimization of the generalized total variation (gTV) over M, subject to
some arbitrary (convex) consistency constraints on the linear measurements of
the signal, admits nonuniform L-spline solutions with fewer knots than the
number of measurements. This shows that non-uniform splines are universal
solutions of continuous-domain linear inverse problems with LASSO, L1, or
TV-like regularization constraints. Remarkably, the spline-type is fully
determined by the choice of L and does not depend on the actual nature of the
measurements.Comment: 28 pages, 1 figur
Single Image Action Recognition by Predicting Space-Time Saliency
We propose a novel approach based on deep Convolutional Neural Networks (CNN)
to recognize human actions in still images by predicting the future motion, and
detecting the shape and location of the salient parts of the image. We make the
following major contributions to this important area of research: (i) We use
the predicted future motion in the static image (Walker et al., 2015) as a
means of compensating for the missing temporal information, while using the
saliency map to represent the the spatial information in the form of location
and shape of what is predicted as significant. (ii) We cast action
classification in static images as a domain adaptation problem by transfer
learning. We first map the input static image to a new domain that we refer to
as the Predicted Optical Flow-Saliency Map domain (POF-SM), and then fine-tune
the layers of a deep CNN model trained on classifying the ImageNet dataset to
perform action classification in the POF-SM domain. (iii) We tested our method
on the popular Willow dataset. But unlike existing methods, we also tested on a
more realistic and challenging dataset of over 2M still images that we
collected and labeled by taking random frames from the UCF-101 video dataset.
We call our dataset the UCF Still Image dataset or UCFSI-101 in short. Our
results outperform the state of the art
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