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A spin on compressive sensing imaging : reticle-based single-pixel imaging system
Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2024.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2026
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| _version_ | 1867613446113067008 |
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| access_status_str | Open Access |
| author2 | Du Plessis, Warren Paul |
| author_browse | Du Plessis, Warren Paul |
| author_facet | Du Plessis, Warren Paul |
| collection | Thesis |
| dc_rights_str_mv | © 2025 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/110453 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:14.878Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/110453 A spin on compressive sensing imaging : reticle-based single-pixel imaging system Du Plessis, Warren Paul u17048657@tuks.co.za Van der Merwe, Marco Marselle UCTD Compressive sensing (CS) Coded aperture Single-pixel imaging (SPI) Genetic algorithm (GA) Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2024. Various aspects of a single-pixel imaging (SPI) system are analyzed using a spinning reticle often referred to as a spinning coded aperture. The cost of imaging outside of the visual spectrum using FPA detectors becomes very expensive and an affordable alternative can have major implications in the commercial and military markets leading to more affordable imaging systems. The first documented use of a spinning disk imaging system came from the development of infrared (IR) missile seeker heads during the Second World War. These systems modulated the incoming light in amplitude of frequency to track a target. The use of spinning frequency modulation (FM) reticles as imaging devices was analyzed to determine the capability of such a system in general imaging scenarios. It was found that the frequency versus angle and radius reticle, termed FM-AR reticle, could only image simple scenes consisting of only a few targets. Using the simpler frequency versus radius reticle and modulating only a single column of pixels for each reticle rotation proved to be a viable option for imaging a wider range of scenes. However, the major drawback was that the imaging time to produce a single image was a factor of the number of columns in the image and the use of compressive sensing techniques was investigated as imaging of the entire scene in one reticle rotation was desired. compressive sensing (CS) is a signal acquisition technique to recover a sparse vector from only a few linear measurements. CS assumes a sparse vector being sampled and the use of sparsifying dictionaries is required for sampling non-sparse vectors such as images. Optimizing the sensing matrix to improve the recovery quality of the sparse vector uses coherence or cumulative coherence to determine a theoretical bound on the signal sparsity to guarantee successful recovery. SPI imaging systems employing CS techniques typically use a digital-micromirror-device (DMD) to modulate the incoming light and random binary patterns are often used. Using a reticle instead of a DMD has the potential to reduce the cost of such SPI systems as DMDs operating outside of the visual spectrum are also very expensive. The two types of reticle patterns considered related to where the patterns are constructed on the reticle. The edge-coded reticle has the reticle pattern constructed radially onto the surface and shifted towards the edge of the disk. The full-coded reticle used the entire surface for the reticle pattern. The edge-coded reticle was found to be a better alternative to the full-coded reticle as the edge-coded reticle allows controlling the CS compression ratio independently of the image dimension. Furthermore, the edge-coded reticle produces an image with a more consistent resolution compared to the full-coded reticle which consists of very small inner pixel dimensions and larger outer pixel dimensions. The genetic algorithm (GA) algorithm is a population-based metaheuristic method based on the Darwinian theory of survival of the fittest. The GA also allows the optimization of binary patterns that correspond to the opaque and transparent pixels on the reticle. Recovering sparse vectors with sensing matrices optimized by the GA for coherence and cumulative coherence generally showed improved reconstruction quality using sparse recovery algorithms. Op- timized sensing matrices consisting of floating point values showed improved recovery quality using OMP but the binary sensing matrices showed higher recovery quality using basis pursuit recovery algorithms. It was observed that the restrictions posed on the binary sensing matrix using an edge-coded reticle did not limit the resulting sensing matrix recovery ability and even improved the capability compared to the non-restricted binary sensing matrices. Training a sensing matrix on a separate image set, referred to as GA-TRAIN, using a sparse recovery algorithm to determine an average fitness score for each candidate in the GA’s population proved to be the better sensing matrix optimization technique. Using GA-TRAIN, improvements of up to 53.1% were observed compared to the GA optimizing coherence or cumulative coherence. Recovering real images using an edge-coded reticle only showed improvements comparing the first to final sensing matrix using the GA with the dB1 Daubechies wavelet dictionary in its second decomposition level (2dB1). This was only observed on the ImageNet dataset for optimizing coherence and cumulative coherence. The other dictionaries considered in this work, namely 1dB4, 5dB1 wavelet dictionary and the discrete cosine transform (DCT) dictionary, showed no noteworthy improvements in reconstruction quality when optimizing coherence and cumulative coherence on the MNIST and ImageNet datasets. The GA-TRAIN method was again used to train sensing matrices on real images and improved recovery quality was observed for all dictionaries and recovery algorithms except for the 2dB1 dictionary using GA-TRAIN. The coherence and cumulative coherence optimization showed slightly improved recovery quality on the ImageNet dataset compared to GA-TRAIN optimization method for the 2dB1 dictionary. It was generally noted that wavelet dictionaries with decomposition levels of one and two proved superior in sparse recovery compared to higher-level decomposition wavelets and the DCT in certain scenarios. The weight of a binary sensing matrix directly influences the signal-to-noise ratio (SNR) of the sampled vector and introducing noise into the sampling vector saw the coherence and cumulative coherence optimized sensing matrices struggle more than the GA-TRAIN optimized sensing matrix to accurately reconstruct the signals. Electrical, Electronic and Computer Engineering MEng (Electronic Engineering) Unrestricted Faculty of Engineering, Built Environment and Information Technology 2026-06-05T12:46:08Z 2026-06-05T12:46:08Z 2025-07 2024-09 Dissertation A2025 http://hdl.handle.net/2263/110453 en © 2025 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Compressive sensing (CS) Coded aperture Single-pixel imaging (SPI) Genetic algorithm (GA) A spin on compressive sensing imaging : reticle-based single-pixel imaging system |
| title | A spin on compressive sensing imaging : reticle-based single-pixel imaging system |
| title_full | A spin on compressive sensing imaging : reticle-based single-pixel imaging system |
| title_fullStr | A spin on compressive sensing imaging : reticle-based single-pixel imaging system |
| title_full_unstemmed | A spin on compressive sensing imaging : reticle-based single-pixel imaging system |
| title_short | A spin on compressive sensing imaging : reticle-based single-pixel imaging system |
| title_sort | spin on compressive sensing imaging reticle based single pixel imaging system |
| topic | UCTD Compressive sensing (CS) Coded aperture Single-pixel imaging (SPI) Genetic algorithm (GA) |
| url | http://hdl.handle.net/2263/110453 |