A compressive sensing based transmissive single-pixel camera

被引:2
作者
Magalhaes, Filipe [1 ,2 ]
Abolbashari, Mehrdad [3 ,4 ]
Farahi, Faramarz [3 ,4 ]
Araujo, Francisco M. [1 ]
Correia, Miguel V. [1 ,2 ]
机构
[1] INESC Porto, Rua Campo Alegre 687, P-4169007 Oporto, Portugal
[2] Univ Porto, Fac Engenharia, Dept Engenharia Electrotecnica Computadores, P-4200 Oporto, Portugal
[3] Univ N Carolina, Ctr Optoelect & Opt Commun, Charlotte, NC 28223 USA
[4] Univ N Carolina, Dept Phys & Opt Sci, Charlotte, NC 28223 USA
来源
INTERNATIONAL CONFERENCE ON APPLICATIONS OF OPTICS AND PHOTONICS | 2011年 / 8001卷
关键词
Compressive Sensing; Single-pixel Camera; Computational Imaging; l(1)-norm; convex optimization; SIGNAL RECOVERY; RECONSTRUCTION;
D O I
10.1117/12.891940
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Compressive sensing (CS) has recently emerged and is now a subject of increasing research and discussion, undergoing significant advances at an incredible pace. The novel theory of CS provides a fundamentally new approach to data acquisition which overcomes the common wisdom of information theory, specifically that provided by the Shannon-Nyquist sampling theorem. Perhaps surprisingly, it predicts that certain signals or images can be accurately, and sometimes even exactly, recovered from what was previously believed to be highly incomplete measurements (information). As the requirements of many applications nowadays often exceed the capabilities of traditional imaging architectures, there has been an increasing deal of interest in so-called computational imaging (CI). CI systems are hybrid imagers in which computation assumes a central role in the image formation process. Therefore, in the light of CS theory, we present a transmissive single-pixel camera that integrates a liquid crystal display (LCD) as an incoherent random coding device, yielding CS-typical compressed observations, since the beginning of the image acquisition process. This camera has been incorporated into an optical microscope and the obtained results can be exploited towards the development of compressive-sensing-based cameras for pixel-level adaptive gain imaging or fluorescence microscopy.
引用
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页数:8
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