608 research outputs found
Sparsity Properties of Compressive Video Sampling Generated by Coefficient Thresholding
We study the compressive sampling (CS) and its application in video encoding framework. The video input is firstly transformed into suitable domain in order to achieve sparser configuration of coefficients. Then, we apply coefficient thresholding to classify which frames to be sampled compressively or conventionally. For frames chosen to undergo compressive sampling, the coefficient vectors will be projected into smaller vectors using random measurement matrix. As CS requires two main conditions, i.e. sparsity and matrix incoherence, this research is emphasized on the enhancement of sparsity property of the input signal. It was empirically proven that the sparsity enhancement could be reached by applying motion compensation and thresholding to the non-significant coefficient count. At the decoder side, the reconstruction algorithm can employ basis pursuit or L1 minimization algorithm
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI
Off-resonance artifacts in magnetic resonance imaging (MRI) are visual
distortions that occur when the actual resonant frequencies of spins within the
imaging volume differ from the expected frequencies used to encode spatial
information. These discrepancies can be caused by a variety of factors,
including magnetic field inhomogeneities, chemical shifts, or susceptibility
differences within the tissues. Such artifacts can manifest as blurring,
ghosting, or misregistration of the reconstructed image, and they often
compromise its diagnostic quality. We propose to resolve these artifacts by
lifting the 2D MRI reconstruction problem to 3D, introducing an additional
"spectral" dimension to model this off-resonance. Our approach is inspired by
recent progress in modeling radiance fields, and is capable of reconstructing
both static and dynamic MR images as well as separating fat and water, which is
of independent clinical interest. We demonstrate our approach in the context of
PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced
Reconstruction) MRI acquisitions, which are popular for their robustness to
motion artifacts. Our method operates in a few minutes on a single GPU, and to
our knowledge is the first to correct for chemical shift in gradient echo
PROPELLER MRI reconstruction without additional measurements or pretraining
data.Comment: Code is available at
https://github.com/sarafridov/volumetric-propelle
Recent advances in transient imaging: A computer graphics and vision perspective
Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation
Recent advances in transient imaging: A computer graphics and vision perspective
Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation
Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision
Signal capture stands in the forefront to perceive and understand the
environment and thus imaging plays the pivotal role in mobile vision. Recent
explosive progresses in Artificial Intelligence (AI) have shown great potential
to develop advanced mobile platforms with new imaging devices. Traditional
imaging systems based on the "capturing images first and processing afterwards"
mechanism cannot meet this unprecedented demand. Differently, Computational
Imaging (CI) systems are designed to capture high-dimensional data in an
encoded manner to provide more information for mobile vision systems.Thanks to
AI, CI can now be used in real systems by integrating deep learning algorithms
into the mobile vision platform to achieve the closed loop of intelligent
acquisition, processing and decision making, thus leading to the next
revolution of mobile vision.Starting from the history of mobile vision using
digital cameras, this work first introduces the advances of CI in diverse
applications and then conducts a comprehensive review of current research
topics combining CI and AI. Motivated by the fact that most existing studies
only loosely connect CI and AI (usually using AI to improve the performance of
CI and only limited works have deeply connected them), in this work, we propose
a framework to deeply integrate CI and AI by using the example of self-driving
vehicles with high-speed communication, edge computing and traffic planning.
Finally, we outlook the future of CI plus AI by investigating new materials,
brain science and new computing techniques to shed light on new directions of
mobile vision systems
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