Paper
19 December 2013 Optimized phase encoding for optical compressive sensing imaging
Author Affiliations +
Abstract
Compressive Sensing (CS) indicates that when the signal of interest is sparse or compressible (i.e., sparse after mathematical transformation), one can take a small number of linear projection measurements from the signal, and reconstruct the signal almost perfectly through proper algorithm. The feature of the CS has great potential applications in that high-resolution imaging is highly desirable while large size detector array is unavailable, such as those in ultraviolet or infrared wavelength region or that in aircrafts and satellites working condition when the data transmission is a key issue.

However, CS technique still faces challenges in the signal sampling and reconstruction. Firstly, detector measurements must be nonnegative in linear optical system which is different from digital image processing. Secondly, blurring caused by practical optical system should be considered, which will destroy the effect of reconstruction.

In this paper, we discuss some kinds of phase encoding which could be used in practice imaging system. We make a compensation to solve the non-negative problem when CS applied in the practical optical system, use a small size detector to receive a general image degrading model, and reconstructed image from the single, low-solution and noisy observation through a fast and feasible non-linear algorithm, the result proves our system is robust and feasible.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jianchao Sun and Feng Xu "Optimized phase encoding for optical compressive sensing imaging", Proc. SPIE 9046, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 904612 (19 December 2013); https://doi.org/10.1117/12.2037012
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Cited by 1 scholarly publication and 1 patent.
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KEYWORDS
Compressed sensing

Computer programming

Imaging systems

Sensors

Optical imaging

Reconstruction algorithms

Charge-coupled devices

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