Nuit Blanche: CSHardware: Development of sparse coding and reconstruction subsystems for astronomical imaging, João Rino-Silvestre

Tuesday, April 23, 2019

CSHardware: Development of sparse coding and reconstruction subsystems for astronomical imaging, João Rino-Silvestre

João just let me know the following:

Salut Igor,

I've noticed that you have a blog dedicated to compressed sensing, so I thought that my MSc dissertation (in which I detail the development of subsystems and of a stand-alone instrument prototype) might be of interest to you and your community. I leave you the link below:
http://hdl.handle.net/10451/37722

Best regards,
João Rino-Silvestre

Thanks João ! Here is his master's thesis: Development of sparse coding and reconstruction subsystems for astronomical imaging by João Silvestre

Compressed sensing (CS) is a revolutionary signal processing technique that allows us, under a specific set of conditions, to fully reconstruct an under-sampled signal. Very early, from its inception, in 2006, to subsequent development and propagation in the following years compressed sensing enabled advancements in photography, holography and medical instrumentation among others. Its application in astronomy however, even though some calls to action have recently been made, has failed to leave the test bed. Continuing from the work developed by Bandarra and Pires in their respective Masters’ dissertations, advancements will here be described on the development of a physical, out of the table, instrument; a compressed sensing astronomy camera (COSAC). Such an instrument was projected to be constituted by five subsystems: optomechanics, signal coding, acquisition electronics, signal reconstruction and the mechanical structure (casing and inner supports for the aforementioned subsystems). The present work focused on the development/implementation of signal coding and reconstruction subsystems, while a simple prototype for the mechanical structure is also proposed to enable testing the instrument in a real world setting; this required the redesign of the optomechanical supports. Additionally, some changes were made to the acquisition electronics in order to not only improve its behavior, but to also facilitate its integration with the signal coding subsystem; as a result two working circuit are proposed, one using an ADC of 10 bits resolution, the other an ADC of 24 bits . A central component to this instrument, which bridges the optomechanics, signal coding and acquisition subsystems, is a digital micromirror device (DMD), an array of independently controlled micromirrors which can be tilted in two, opposed, directions. Such a device can, and is, thus used to manipulate light. For this project a DLP LightCrafter, a projector development kit by Texas Instruments which includes a DMD, was used to encode light signals. The signal coding subsystem is constituted by the LightCrafter and two programs: one written in C/C++ to run either on a PC (DMD-CS.cpp), which main purpose is to control the DMD and communicate with the LightCrafter’s processor, and which also communicates with an Arduino micro-controller that manages the acquisition electronics; the second (which is also part of the acquisition subsystem) in Arduino programming language, to run on the micro-controller (pIDDO.ino), which will manage the processes required to perform measurements with the electronics and communicate with the C/C++ program; the interactions between both programs are crucial to ensure synchronism between the signal coding and acquisition subsystems. The chosen encoding basis are squared Hadamard matrices that can be attained by following simple algorithms; rows of such matrices were then manipulated into tilt configurations for the micromirror grid; the set of rows used will constitute a sampling matrix. Each program outputs a file, one holding information about the sampling matrix used, the other holding the measurements. The signal reconstruction subsystem is another program that takes the files generated by DMD-CS.cpp and pIDDO.ino to reconstruct the original signal by implementing a Matlab script written by Romberg. The program then outputs a BMP image file of that reconstruction. The components of the prototype structural subsystem and optomechanical supports were designed using computer assisted design (CAD) software, with which finite element simulations were also performed to ensure those same components would be able to endure real world conditions. Some of these components were bought most of them were fabricated in the laboratory. All subsystems were individually tested, as well as in couples (when relevant). After passing those tests, these subsystems were assembled to form COSAC. The instrument was calibrated, analysed and validated, using both versions of the acquisition circuit, in a laboratory setting with controlled lighting conditions. Comparative results of COSAC’s performance for three modes of acquisition (raster, Hadamard transform optics and CS with Hadamard base) are also presented. COSAC was shown to be able to produce images of CS measurements, performed in the visible spectrum, with at least 64_64 pixels.

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