Single-pixel imaging with Morlet wavelet correlated random patterns

Single-pixel imaging is an indirect imaging technique which utilizes simplified optical hardware and advanced computational methods. It offers novel solutions for hyper-spectral imaging, polarimetric imaging, three-dimensional imaging, holographic imaging, optical encryption and imaging through scat...

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Bibliographic Details
Published in:Scientific reports 2018-01, Vol.8 (1), p.466-466, Article 466
Main Authors: Czajkowski, Krzysztof M., Pastuszczak, Anna, Kotyński, Rafał
Format: Article
Language:English
Subjects:
639/624/1075/146
639/624/1107/510
Algorithms
Cameras
Compression
Computer applications
Fourier transforms
Frequency dependence
Humanities and Social Sciences
multidisciplinary
Sampling
Science
Science (multidisciplinary)
Sensors
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Summary:Single-pixel imaging is an indirect imaging technique which utilizes simplified optical hardware and advanced computational methods. It offers novel solutions for hyper-spectral imaging, polarimetric imaging, three-dimensional imaging, holographic imaging, optical encryption and imaging through scattering media. The main limitations for its use come from relatively high measurement and reconstruction times. In this paper we propose to reduce the required signal acquisition time by using a novel sampling scheme based on a random selection of Morlet wavelets convolved with white noise. While such functions exhibit random properties, they are locally determined by Morlet wavelet parameters. The proposed method is equivalent to random sampling of the properly selected part of the feature space, which maps the measured images accurately both in the spatial and spatial frequency domains. We compare both numerically and experimentally the image quality obtained with our sampling protocol against widely-used sampling with Walsh-Hadamard or noiselet functions. The results show considerable improvement over the former methods, enabling single-pixel imaging at low compression rates on the order of a few percent.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-18968-6