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The objective of this work is to use the compressive sensing in the field of the space exploration.
The compressive sensing theory affirms that an image can be retrieved taking only fewer mea-
surement with respect to the minimum number dictated by the Nyquist theory. Contrarily to
the classical method of acquisition of an image, the CS technique allows to create lensless cam-
eras like the Flatcam, the NoRDS-CAIC and the DiffuserCam. The drawback of this technique
is the need of a decoding algorithm for the reconstruction of the original image.
The reconstruction method of the images is not unique, there are classical methods, that use
the total variation minimization, and the deep learning methods. This work analyzes and com-
pares two classical and two deep learning methods in order to find the best method for the
space application.
The simulations have found that the method using the deep learning approach give optimum
results. The images can be well-reconstructed already with a number of measurement that is
the 30% of the size of the images in less than one second.
In order to practically understand the principle of the compressive sensing, an example of the
DiffuserCam has been constructed in the laboratory. The camera is composed only by a diffuser
and a sensor. The experience gave great results, the images have been reconstructed with great
quality in short time.
Finally, the compressive sensing seems to be fascinating for the space application. This tech-
nique allows to suppress the compressing board because the data are taken already compressed.
The suppression of the compression board reduces the mass and especially the power budgets.
Moreover, the post-processing on board allows the reduction of the downlink transmission.
The compressive sensing in space exploration finds its application especially in the infrared
spectral band. In fact, the infrared detectors are too expensive and the compressive sensing
instruments uses the single pixel detectors that are cheaper

compressive sensing --- lensless imaging --- Diffusercam --- Ingénierie, informatique & technologie > Ingénierie aérospatiale

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This master thesis explores the application of compressive sensing in satellite Earth
observation instruments. Firstly, a general state of the art of compressive sensing is
made by introducing the mathematical concepts and describing some existing designs
that implement the method. The essence of compressive sensing consists in reconstructing
images with fewer measurements than in classical imaging. The method can bring drastic
reduction of data quantity requirements and detector sizes as well as an increase of spatial
resolution. These advantages are particularly interesting in Earth observation instruments
considering the vast amount of data that they generate and the size limitations of satellites.
This is even more considerable in the infrared spectrum where detectors are typically
large.
A deep learning compressive sensing reconstruction algorithm dubbed ISTA-Net+ is
tested an proved to work on satellite multispectral data during simulations. Finally, a
complete compressive sensing experimental chain has been implemented within laboratory
environment. For the reconstruction, the hardware-compressed data could not be passed to
the ISTA-Net+ algorithm, thus a simpler algorithm applying an inpainting using iterative
hard thresholding is applied. The experiment is satisfactory and the method is proven to
work. Nonetheless, the optical system has to be optimized and a more efficient algorithm
must be implemented. Therefore, this work opens the way to further improvements and
investigations.

Compressive Sensing --- Earth Observation --- Signal processing --- Optics --- Physique, chimie, mathématiques & sciences de la terre > Aérospatiale, astronomie & astrophysique

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This first book on greedy approximation gives a systematic presentation of the fundamental results. It also contains an introduction to two hot topics in numerical mathematics: learning theory and compressed sensing. Nonlinear approximation is becoming increasingly important, especially since two types are frequently employed in applications: adaptive methods are used in PDE solvers, while m-term approximation is used in image/signal/data processing, as well as in the design of neural networks. The fundamental question of nonlinear approximation is how to devise good constructive methods (algorithms) and recent results have established that greedy type algorithms may be the solution. The author has drawn on his own teaching experience to write a book ideally suited to graduate courses. The reader does not require a broad background to understand the material. Important open problems are included to give students and professionals alike ideas for further research.

Approximation theory. --- Theory of approximation --- Functional analysis --- Functions --- Polynomials --- Chebyshev systems --- Compressed sensing (Telecommunication) --- Compressive sensing (Telecommunication) --- Sensing, Compressed (Telecommunication) --- Sparse sampling (Telecommunication) --- Signal processing

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Optical fiber communication --- Compressed sensing (Telecommunication) --- Quality control. --- Compressive sensing (Telecommunication) --- Sensing, Compressed (Telecommunication) --- Sparse sampling (Telecommunication) --- Signal processing --- Fiber-optic communication --- Fiber optic telecommunication --- Optical communications --- Fiber optics

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Quantum optics has received a lot of attention in recent decades due to the handiness and versatility of optical systems, which have been exploited both to study the foundations of quantum mechanics and for various applications. In this Special Issue, we collect some articles and a review focusing on some research activities that show the potential of quantum optics in the advancement of quantum technologies.

Research & information: general --- Physics --- integrated photonics --- quantum optics --- quantum simulation --- mesoscopic quantum states of light --- nonclassical photon-number correlations --- lossy transmission channels --- quantum state engineering --- nonlinear interferometer --- spontaneous four-wave mixing --- quantum message authentication --- quantum three-pass protocol --- Gao’s forgery --- swap test --- conditional states --- silicon photomultipliers --- optical cross-talk --- nonclassicality --- quantum imaging --- plenoptic imaging --- quantum correlations --- SPAD arrays --- quantum fisher information --- compressive sensing --- quantum finite automata --- periodic languages --- confidence amplification --- photodetection --- n/a --- Gao's forgery