3,960 research outputs found
Exploiting flow dynamics for super-resolution in contrast-enhanced ultrasound
Ultrasound localization microscopy offers new radiation-free diagnostic tools
for vascular imaging deep within the tissue. Sequential localization of echoes
returned from inert microbubbles with low-concentration within the bloodstream
reveal the vasculature with capillary resolution. Despite its high spatial
resolution, low microbubble concentrations dictate the acquisition of tens of
thousands of images, over the course of several seconds to tens of seconds, to
produce a single super-resolved image. %since each echo is required to be well
separated from adjacent microbubbles. Such long acquisition times and stringent
constraints on microbubble concentration are undesirable in many clinical
scenarios. To address these restrictions, sparsity-based approaches have
recently been developed. These methods reduce the total acquisition time
dramatically, while maintaining good spatial resolution in settings with
considerable microbubble overlap. %Yet, non of the reported methods exploit the
fact that microbubbles actually flow within the bloodstream. % to improve
recovery. Here, we further improve sparsity-based super-resolution ultrasound
imaging by exploiting the inherent flow of microbubbles and utilize their
motion kinematics. While doing so, we also provide quantitative measurements of
microbubble velocities. Our method relies on simultaneous tracking and
super-localization of individual microbubbles in a frame-by-frame manner, and
as such, may be suitable for real-time implementation. We demonstrate the
effectiveness of the proposed approach on both simulations and {\it in-vivo}
contrast enhanced human prostate scans, acquired with a clinically approved
scanner.Comment: 11 pages, 9 figure
Sparsity based sub-wavelength imaging with partially incoherent light via quadratic compressed sensing
We demonstrate that sub-wavelength optical images borne on
partially-spatially-incoherent light can be recovered, from their far-field or
from the blurred image, given the prior knowledge that the image is sparse, and
only that. The reconstruction method relies on the recently demonstrated
sparsity-based sub-wavelength imaging. However, for
partially-spatially-incoherent light, the relation between the measurements and
the image is quadratic, yielding non-convex measurement equations that do not
conform to previously used techniques. Consequently, we demonstrate new
algorithmic methodology, referred to as quadratic compressed sensing, which can
be applied to a range of other problems involving information recovery from
partial correlation measurements, including when the correlation function has
local dependencies. Specifically for microscopy, this method can be readily
extended to white light microscopes with the additional knowledge of the light
source spectrum.Comment: 16 page
Phase Retrieval with Application to Optical Imaging
This review article provides a contemporary overview of phase retrieval in
optical imaging, linking the relevant optical physics to the information
processing methods and algorithms. Its purpose is to describe the current state
of the art in this area, identify challenges, and suggest vision and areas
where signal processing methods can have a large impact on optical imaging and
on the world of imaging at large, with applications in a variety of fields
ranging from biology and chemistry to physics and engineering
Super-resolution far-field ghost imaging via compressive sampling
Much more image details can be resolved by improving the system's imaging
resolution and enhancing the resolution beyond the system's Rayleigh
diffraction limit is generally called super-resolution. By combining the sparse
prior property of images with the ghost imaging method, we demonstrated
experimentally that super-resolution imaging can be nonlocally achieved in the
far field even without looking at the object. Physical explanation of
super-resolution ghost imaging via compressive sampling and its potential
applications are also discussed.Comment: 4pages,4figure
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To overcome this problem, we propose a simple yet effective sparse
convolution layer which explicitly considers the location of missing data
during the convolution operation. We demonstrate the benefits of the proposed
network architecture in synthetic and real experiments with respect to various
baseline approaches. Compared to dense baselines, the proposed sparse
convolution network generalizes well to novel datasets and is invariant to the
level of sparsity in the data. For our evaluation, we derive a novel dataset
from the KITTI benchmark, comprising 93k depth annotated RGB images. Our
dataset allows for training and evaluating depth upsampling and depth
prediction techniques in challenging real-world settings and will be made
available upon publication
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