10,093 research outputs found
Fourier Magnetic Imaging with Nanoscale Resolution and Compressed Sensing Speed-up using Electronic Spins in Diamond
Optically-detected magnetic resonance using Nitrogen Vacancy (NV) color
centres in diamond is a leading modality for nanoscale magnetic field imaging,
as it provides single electron spin sensitivity, three-dimensional resolution
better than 1 nm, and applicability to a wide range of physical and biological
samples under ambient conditions. To date, however, NV-diamond magnetic imaging
has been performed using real space techniques, which are either limited by
optical diffraction to 250 nm resolution or require slow, point-by-point
scanning for nanoscale resolution, e.g., using an atomic force microscope,
magnetic tip, or super-resolution optical imaging. Here we introduce an
alternative technique of Fourier magnetic imaging using NV-diamond. In analogy
with conventional magnetic resonance imaging (MRI), we employ pulsed magnetic
field gradients to phase-encode spatial information on NV electronic spins in
wavenumber or k-space followed by a fast Fourier transform to yield real-space
images with nanoscale resolution, wide field-of-view (FOV), and compressed
sensing speed-up.Comment: 31 pages, 10 figure
Process of image super-resolution
In this paper we explain a process of super-resolution reconstruction
allowing to increase the resolution of an image.The need for high-resolution
digital images exists in diverse domains, for example the medical and spatial
domains. The obtaining of high-resolution digital images can be made at the
time of the shooting, but it is often synonymic of important costs because of
the necessary material to avoid such costs, it is known how to use methods of
super-resolution reconstruction, consisting from one or several low resolution
images to obtain a high-resolution image. The american patent US 9208537
describes such an algorithm. A zone of one low-resolution image is isolated and
categorized according to the information contained in pixels forming the
borders of the zone. The category of it zone determines the type of
interpolation used to add pixels in aforementioned zone, to increase the
neatness of the images. It is also known how to reconstruct a low-resolution
image there high-resolution image by using a model of super-resolution
reconstruction whose learning is based on networks of neurons and on image or a
picture library. The demand of chinese patent CN 107563965 and the scientist
publication "Pixel Recursive Super Resolution", R. Dahl, M. Norouzi, J. Shlens
propose such methods. The aim of this paper is to demonstrate that it is
possible to reconstruct coherent human faces from very degraded pixelated
images with a very fast algorithm, more faster than compressed sensing (CS),
easier to compute and without deep learning, so without important technology
resources, i.e. a large database of thousands training images (see
arXiv:2003.13063).
This technological breakthrough has been patented in 2018 with the demand of
French patent FR 1855485 (https://patents.google.com/patent/FR3082980A1, see
the HAL reference https://hal.archives-ouvertes.fr/hal-01875898v1).Comment: 19 pages, 10 figure
Single-shot compressed ultrafast photography: a review
Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
Non-Local Total Variation (NLTV) has emerged as a useful tool in variational
methods for image recovery problems. In this paper, we extend the NLTV-based
regularization to multicomponent images by taking advantage of the Structure
Tensor (ST) resulting from the gradient of a multicomponent image. The proposed
approach allows us to penalize the non-local variations, jointly for the
different components, through various matrix norms with .
To facilitate the choice of the hyper-parameters, we adopt a constrained convex
optimization approach in which we minimize the data fidelity term subject to a
constraint involving the ST-NLTV regularization. The resulting convex
optimization problem is solved with a novel epigraphical projection method.
This formulation can be efficiently implemented thanks to the flexibility
offered by recent primal-dual proximal algorithms. Experiments are carried out
for multispectral and hyperspectral images. The results demonstrate the
interest of introducing a non-local structure tensor regularization and show
that the proposed approach leads to significant improvements in terms of
convergence speed over current state-of-the-art methods
Generalized Inpainting Method for Hyperspectral Image Acquisition
A recently designed hyperspectral imaging device enables multiplexed
acquisition of an entire data volume in a single snapshot thanks to
monolithically-integrated spectral filters. Such an agile imaging technique
comes at the cost of a reduced spatial resolution and the need for a
demosaicing procedure on its interleaved data. In this work, we address both
issues and propose an approach inspired by recent developments in compressed
sensing and analysis sparse models. We formulate our superresolution and
demosaicing task as a 3-D generalized inpainting problem. Interestingly, the
target spatial resolution can be adjusted for mitigating the compression level
of our sensing. The reconstruction procedure uses a fast greedy method called
Pseudo-inverse IHT. We also show on simulations that a random arrangement of
the spectral filters on the sensor is preferable to regular mosaic layout as it
improves the quality of the reconstruction. The efficiency of our technique is
demonstrated through numerical experiments on both synthetic and real data as
acquired by the snapshot imager.Comment: Keywords: Hyperspectral, inpainting, iterative hard thresholding,
sparse models, CMOS, Fabry-P\'ero
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