106,758 research outputs found
Soft thresholding schemes for multiple signal classification algorithm
Multiple signal classification algorithm (MUSICAL) exploits temporal
fluctuations in fluorescence intensity to perform super-resolution microscopy
by computing the value of a super-resolving indicator function across a fine
sample grid. A key step in the algorithm is the separation of the measurements
into signal and noise subspaces, based on a single user-specified parameter
called the threshold. The resulting image is strongly sensitive to this
parameter and the subjectivity arising from multiple practical factors makes it
difficult to determine the right rule of selection. We address this issue by
proposing soft thresholding schemes derived from a new generalized framework
for indicator function design. We show that the new schemes significantly
alleviate the subjectivity and sensitivity of hard thresholding while retaining
the super-resolution ability. We also evaluate the trade-off between resolution
and contrast and the out-of-focus light rejection using the various indicator
functions. Through this, we create significant new insights into the use and
further optimization of MUSICAL for a wide range of practical scenarios.Comment: 15 pages, 5 figure
Super-resolution generative adversarial networks of randomly-seeded fields
Reconstruction of field quantities from sparse measurements is a problem arising in a broad spectrum of applications. This task is particularly challenging when the mapping between sparse measurements and field quantities is performed in an unsupervised manner. Further complexity is added for moving sensors and/or random on–off status. Under such conditions, the most straightforward solution is to interpolate the scattered data onto a regular grid. However, the spatial resolution achieved with this approach is ultimately limited by the mean spacing between the sparse measurements. In this work, we propose a super-resolution generative adversarial network framework to estimate field quantities from random sparse sensors. The algorithm exploits random sampling to provide incomplete views of the high-resolution underlying distributions. It is hereby referred to as the randomly seeded super-resolution generative adversarial network (RaSeedGAN). The proposed technique is tested on synthetic databases of fluid flow simulations, ocean surface temperature distribution measurements and particle-image velocimetry data of a zero-pressure-gradient turbulent boundary layer. The results show excellent performance even in cases with high sparsity or noise level. This generative adversarial network algorithm provides full-field high-resolution estimation from randomly seeded fields with no need of full-field high-resolution representations for training.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 949085) received by S.D. NOAA High Resolution SST data provided by the NOAA/OAR/ESRL PSL
Super-resolution Line Spectrum Estimation with Block Priors
We address the problem of super-resolution line spectrum estimation of an
undersampled signal with block prior information. The component frequencies of
the signal are assumed to take arbitrary continuous values in known frequency
blocks. We formulate a general semidefinite program to recover these
continuous-valued frequencies using theories of positive trigonometric
polynomials. The proposed semidefinite program achieves super-resolution
frequency recovery by taking advantage of known structures of frequency blocks.
Numerical experiments show great performance enhancements using our method.Comment: 7 pages, double colum
Adaptive foveated single-pixel imaging with dynamic super-sampling
As an alternative to conventional multi-pixel cameras, single-pixel cameras
enable images to be recorded using a single detector that measures the
correlations between the scene and a set of patterns. However, to fully sample
a scene in this way requires at least the same number of correlation
measurements as there are pixels in the reconstructed image. Therefore
single-pixel imaging systems typically exhibit low frame-rates. To mitigate
this, a range of compressive sensing techniques have been developed which rely
on a priori knowledge of the scene to reconstruct images from an under-sampled
set of measurements. In this work we take a different approach and adopt a
strategy inspired by the foveated vision systems found in the animal kingdom -
a framework that exploits the spatio-temporal redundancy present in many
dynamic scenes. In our single-pixel imaging system a high-resolution foveal
region follows motion within the scene, but unlike a simple zoom, every frame
delivers new spatial information from across the entire field-of-view. Using
this approach we demonstrate a four-fold reduction in the time taken to record
the detail of rapidly evolving features, whilst simultaneously accumulating
detail of more slowly evolving regions over several consecutive frames. This
tiered super-sampling technique enables the reconstruction of video streams in
which both the resolution and the effective exposure-time spatially vary and
adapt dynamically in response to the evolution of the scene. The methods
described here can complement existing compressive sensing approaches and may
be applied to enhance a variety of computational imagers that rely on
sequential correlation measurements.Comment: 13 pages, 5 figure
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