19,245 research outputs found
A Learning-Based Framework for Line-Spectra Super-resolution
We propose a learning-based approach for estimating the spectrum of a
multisinusoidal signal from a finite number of samples. A neural-network is
trained to approximate the spectra of such signals on simulated data. The
proposed methodology is very flexible: adapting to different signal and noise
models only requires modifying the training data accordingly. Numerical
experiments show that the approach performs competitively with classical
methods designed for additive Gaussian noise at a range of noise levels, and is
also effective in the presence of impulsive noise.Comment: Accepted at ICASSP 201
Deep learning-based super-resolution in coherent imaging systems
We present a deep learning framework based on a generative adversarial
network (GAN) to perform super-resolution in coherent imaging systems. We
demonstrate that this framework can enhance the resolution of both pixel
size-limited and diffraction-limited coherent imaging systems. We
experimentally validated the capabilities of this deep learning-based coherent
imaging approach by super-resolving complex images acquired using a lensfree
on-chip holographic microscope, the resolution of which was pixel size-limited.
Using the same GAN-based approach, we also improved the resolution of a
lens-based holographic imaging system that was limited in resolution by the
numerical aperture of its objective lens. This deep learning-based
super-resolution framework can be broadly applied to enhance the
space-bandwidth product of coherent imaging systems using image data and
convolutional neural networks, and provides a rapid, non-iterative method for
solving inverse image reconstruction or enhancement problems in optics.Comment: 18 pages, 9 figures, 3 table
Aerial Spectral Super-Resolution using Conditional Adversarial Networks
Inferring spectral signatures from ground based natural images has acquired a
lot of interest in applied deep learning. In contrast to the spectra of ground
based images, aerial spectral images have low spatial resolution and suffer
from higher noise interference. In this paper, we train a conditional
adversarial network to learn an inverse mapping from a trichromatic space to 31
spectral bands within 400 to 700 nm. The network is trained on AeroCampus, a
first of its kind aerial hyperspectral dataset. AeroCampus consists of high
spatial resolution color images and low spatial resolution hyperspectral images
(HSI). Color images synthesized from 31 spectral bands are used to train our
network. With a baseline root mean square error of 2.48 on the synthesized RGB
test data, we show that it is possible to generate spectral signatures in
aerial imagery
Super-Resolution 1H Magnetic Resonance Spectroscopic Imaging utilizing Deep Learning
Magnetic resonance spectroscopic imaging (SI) is a unique imaging technique
that provides biochemical information from in vivo tissues. The 1H spectra
acquired from several spatial regions are quantified to yield metabolite
concentrations reflective of tissue metabolism. However, since these
metabolites are found in tissues at very low concentrations, SI is often
acquired with limited spatial resolution. In this work we test the hypothesis
that deep learning is able to upscale low resolution SI, together with the
T1-weighted (T1w) image, to reconstruct high resolution SI. We report a novel
densely connected Unet (D-Unet) architecture capable of producing
super-resolution spectroscopic images. The inputs for the D-UNet are the T1w
image and the low resolution SI image while the output is the high resolution
SI. The results of the D-UNet are compared both qualitatively and
quantitatively to simulated and in vivo high resolution SI. It is found that
this deep learning approach can produce high quality spectroscopic images and
reconstruct entire 1H spectra from low resolution acquisitions, which can
greatly advance the current SI workflow.Comment: 8 figures, 1 tabl
Hyperspectral recovery from RGB images using Gaussian Processes
We propose to recover spectral details from RGB images of known spectral
quantization by modeling natural spectra under Gaussian Processes and combining
them with the RGB images. Our technique exploits Process Kernels to model the
relative smoothness of reflectance spectra, and encourages non-negativity in
the resulting signals for better estimation of the reflectance values. The
Gaussian Processes are inferred in sets using clusters of spatio-spectrally
correlated hyperspectral training patches. Each set is transformed to match the
spectral quantization of the test RGB image. We extract overlapping patches
from the RGB image and match them to the hyperspectral training patches by
spectrally transforming the latter. The RGB patches are encoded over the
transformed Gaussian Processes related to those hyperspectral patches and the
resulting image is constructed by combining the codes with the original
Processes. Our approach infers the desired Gaussian Processes under a fully
Bayesian model inspired by Beta-Bernoulli Process, for which we also present
the inference procedure. A thorough evaluation using three hyperspectral
datasets demonstrates the effective extraction of spectral details from RGB
images by the proposed technique.Comment: Revision submitted to IEEE TPAM
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
This paper reviews the second challenge on spectral reconstruction from RGB
images, i.e., the recovery of whole-scene hyperspectral (HS) information from a
3-channel RGB image. As in the previous challenge, two tracks were provided:
(i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB
images are themselves calculated numerically using the ground-truth HS images
and supplied spectral sensitivity functions (ii) a "Real World" track,
simulating capture by an uncalibrated and unknown camera, where the HS images
are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever,
natural hyperspectral image data set is presented, containing a total of 510 HS
images. The Clean and Real World tracks had 103 and 78 registered participants
respectively, with 14 teams competing in the final testing phase. A description
of the proposed methods, alongside their challenge scores and an extensive
evaluation of top performing methods is also provided. They gauge the
state-of-the-art in spectral reconstruction from an RGB image
Learned Spectral Super-Resolution
We describe a novel method for blind, single-image spectral super-resolution.
While conventional super-resolution aims to increase the spatial resolution of
an input image, our goal is to spectrally enhance the input, i.e., generate an
image with the same spatial resolution, but a greatly increased number of
narrow (hyper-spectral) wave-length bands. Just like the spatial statistics of
natural images has rich structure, which one can exploit as prior to predict
high-frequency content from a low resolution image, the same is also true in
the spectral domain: the materials and lighting conditions of the observed
world induce structure in the spectrum of wavelengths observed at a given
pixel. Surprisingly, very little work exists that attempts to use this
diagnosis and achieve blind spectral super-resolution from single images. We
start from the conjecture that, just like in the spatial domain, we can learn
the statistics of natural image spectra, and with its help generate finely
resolved hyper-spectral images from RGB input. Technically, we follow the
current best practice and implement a convolutional neural network (CNN), which
is trained to carry out the end-to-end mapping from an entire RGB image to the
corresponding hyperspectral image of equal size. We demonstrate spectral
super-resolution both for conventional RGB images and for multi-spectral
satellite data, outperforming the state-of-the-art.Comment: Submitted to ICCV 2017 (10 pages, 8 figures
Simultaneous Estimation of Noise Variance and Number of Peaks in Bayesian Spectral Deconvolution
The heuristic identification of peaks from noisy complex spectra often leads
to misunderstanding of the physical and chemical properties of matter. In this
paper, we propose a framework based on Bayesian inference, which enables us to
separate multipeak spectra into single peaks statistically and consists of two
steps. The first step is estimating both the noise variance and the number of
peaks as hyperparameters based on Bayes free energy, which generally is not
analytically tractable. The second step is fitting the parameters of each peak
function to the given spectrum by calculating the posterior density, which has
a problem of local minima and saddles since multipeak models are nonlinear and
hierarchical. Our framework enables the escape from local minima or saddles by
using the exchange Monte Carlo method and calculates Bayes free energy via the
multiple histogram method. We discuss a simulation demonstrating how efficient
our framework is and show that estimating both the noise variance and the
number of peaks prevents overfitting, overpenalizing, and misunderstanding the
precision of parameter estimation
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network
The existence of hybrid noise in hyperspectral images (HSIs) severely
degrades the data quality, reduces the interpretation accuracy of HSIs, and
restricts the subsequent HSIs applications. In this paper, the spatial-spectral
gradient network (SSGN) is presented for mixed noise removal in HSIs. The
proposed method employs a spatial-spectral gradient learning strategy, in
consideration of the unique spatial structure directionality of sparse noise
and spectral differences with additional complementary information for better
extracting intrinsic and deep features of HSIs. Based on a fully cascaded
multi-scale convolutional network, SSGN can simultaneously deal with the
different types of noise in different HSIs or spectra by the use of the same
model. The simulated and real-data experiments undertaken in this study
confirmed that the proposed SSGN performs better at mixed noise removal than
the other state-of-the-art HSI denoising algorithms, in evaluation indices,
visual assessments, and time consumption.Comment: Accept by IEEE TGR
Data recovery in computational fluid dynamics through deep image priors
One of the challenges encountered by computational simulations at exascale is
the reliability of simulations in the face of hardware and software faults.
These faults, expected to increase with the complexity of the computational
systems, will lead to the loss of simulation data and simulation failure and
are currently addressed through a checkpoint-restart paradigm. Focusing
specifically on computational fluid dynamics simulations, this work proposes a
method that uses a deep convolutional neural network to recover simulation
data. This data recovery method (i) is agnostic to the flow configuration and
geometry, (ii) does not require extensive training data, and (iii) is accurate
for very different physical flows. Results indicate that the use of deep image
priors for data recovery is more accurate than standard recovery techniques,
such as the Gaussian process regression, also known as Kriging. Data recovery
is performed for two canonical fluid flows: laminar flow around a cylinder and
homogeneous isotropic turbulence. For data recovery of the laminar flow around
a cylinder, results indicate similar performance between the proposed method
and Gaussian process regression across a wide range of mask sizes. For
homogeneous isotropic turbulence, data recovery through the deep convolutional
neural network exhibits an error in relevant turbulent quantities approximately
three times smaller than that for the Gaussian process regression,. Forward
simulations using recovered data illustrate that the enstrophy decay is
captured within 10% using the deep convolutional neural network approach.
Although demonstrated specifically for data recovery of fluid flows, this
technique can be used in a wide range of applications, including particle image
velocimetry, visualization, and computational simulations of physical processes
beyond the Navier-Stokes equations
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