dc.contributor.author |
Stoltz, George G
|
|
dc.contributor.author |
Nel, AL
|
|
dc.date.accessioned |
2021-02-16T12:50:41Z |
|
dc.date.available |
2021-02-16T12:50:41Z |
|
dc.date.issued |
2020-10 |
|
dc.identifier.citation |
Stoltz, G.G. & Nel, A. 2020. Improving spatial domain based image formation through compressed sensing. http://hdl.handle.net/10204/11773 . |
en_ZA |
dc.identifier.isbn |
9781510639140 |
|
dc.identifier.issn |
0277-786X |
|
dc.identifier.issn |
1996-756X |
|
dc.identifier.uri |
http://hdl.handle.net/10204/11773
|
|
dc.description.abstract |
In this paper, we improve image reconstruction in a single-pixel scanning system by selecting an detector optimal field of view. Image reconstruction is based on compressed sensing and image quality is compared to interpolated staring arrays. The image quality comparisons use a dead leaves" data set, Bayesian estimation and the Peak- Signal-to-Noise Ratio (PSNR) measure. Compressed sensing is explored as an interpolation algorithm and shows with high probability an improved performance compared to Lanczos interpolation. Furthermore, multi-level sampling in a single-pixel scanning system is simulated by dynamically altering the detector field of view. It was shown that multi-level sampling improves the distribution of the Peak-Signal-to-Noise Ratio. We further explore the expected sampling level distributions and PSNR distributions for multi-level sampling. The PSNR distribution indicates that there is a small set of levels which will improve image quality over interpolated staring arrays. We further conclude that multi-level sampling will outperform single-level uniform random sampling on average. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://doi.org/10.1117/12.2575435 |
en_US |
dc.relation.uri |
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11549.toc#FrontMatterVolume11549 |
en_US |
dc.relation.uri |
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11549/115490F/Improving-spatial-domain-based-image-formation-through-compressed-sensing/10.1117/12.2575435.short |
en_US |
dc.source |
Proceeding of SPIE, 11549, Advanced Optical Imaging Technologies III, September 2020 (online) |
en_US |
dc.subject |
Image formation |
en_US |
dc.subject |
Compressed sensing |
en_US |
dc.subject |
Single pixel scanning system |
en_US |
dc.subject |
Bayesian estimation |
en_US |
dc.title |
Improving spatial domain based image formation through compressed sensing |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
9 |
en_US |
dc.description.note |
Copyright: 2020 SPIE. Due to copyright restrictions, the attached PDF file contains the accepted version of the published item. For access to the published version, please consult the publisher's website: https://doi.org/10.1117/12.2575435 |
en_US |
dc.description.cluster |
Defence and Security |
|
dc.description.impactarea |
Optronic Sensor Systems |
en_US |
dc.identifier.apacitation |
Stoltz, G. G., & Nel, A. (2020). Improving spatial domain based image formation through compressed sensing. http://hdl.handle.net/10204/11773 |
en_ZA |
dc.identifier.chicagocitation |
Stoltz, George G, and AL Nel. "Improving spatial domain based image formation through compressed sensing." <i>Proceeding of SPIE, 11549, Advanced Optical Imaging Technologies III, September 2020 (online)</i> (2020): http://hdl.handle.net/10204/11773 |
en_ZA |
dc.identifier.vancouvercitation |
Stoltz GG, Nel A, Improving spatial domain based image formation through compressed sensing; 2020. http://hdl.handle.net/10204/11773 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Stoltz, George G
AU - Nel, AL
AB - In this paper, we improve image reconstruction in a single-pixel scanning system by selecting an detector optimal field of view. Image reconstruction is based on compressed sensing and image quality is compared to interpolated staring arrays. The image quality comparisons use a dead leaves" data set, Bayesian estimation and the Peak- Signal-to-Noise Ratio (PSNR) measure. Compressed sensing is explored as an interpolation algorithm and shows with high probability an improved performance compared to Lanczos interpolation. Furthermore, multi-level sampling in a single-pixel scanning system is simulated by dynamically altering the detector field of view. It was shown that multi-level sampling improves the distribution of the Peak-Signal-to-Noise Ratio. We further explore the expected sampling level distributions and PSNR distributions for multi-level sampling. The PSNR distribution indicates that there is a small set of levels which will improve image quality over interpolated staring arrays. We further conclude that multi-level sampling will outperform single-level uniform random sampling on average.
DA - 2020-10
DB - ResearchSpace
DP - CSIR
J1 - Proceeding of SPIE, 11549, Advanced Optical Imaging Technologies III, September 2020 (online)
KW - Image formation
KW - Compressed sensing
KW - Single pixel scanning system
KW - Bayesian estimation
LK - https://researchspace.csir.co.za
PY - 2020
SM - 9781510639140
SM - 0277-786X
SM - 1996-756X
T1 - Improving spatial domain based image formation through compressed sensing
TI - Improving spatial domain based image formation through compressed sensing
UR - http://hdl.handle.net/10204/11773
ER - |
en_ZA |
dc.identifier.worklist |
24001 |
en_US |