Computational improvement in single-pixel imaging contrast and resolution (Conference Presentation)

Robert J. Stokoe; Patrick A. Stockton; Ali Pezeshki; Randy A. Bartels

doi:10.1117/12.2506903

4 March 2019 Computational improvement in single-pixel imaging contrast and resolution (Conference Presentation)

Robert J. Stokoe, Patrick A. Stockton, Ali Pezeshki, Randy A. Bartels

Proceedings Volume 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI; 108830K (2019) https://doi.org/10.1117/12.2506903
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Single-pixel imaging is a developing family of techniques which offer several advantages over conventional imaging with a segmented detector. These include higher speed, improved availability and quality of detectors at long wavelengths. Examples include laser-scanning microscopy, frequency-domain techniques, ghost imaging, and methods employing an orthogonal mask sequence such as Hadamard masks. We analyze this class of imaging techniques in terms of Frame theory, which concerns sets of vectors that span a given vector space but are not linearly independent as in the case of a basis. The use of frames (rather than bases) allows for redundant measurements, which can improve the signal-to-noise ratio (SNR) of the reconstructed image. Current single-pixel techniques admit an intuitive, physically-motivated reconstruction scheme, but the reconstruction method is not always obvious. The analysis provides a prescription for reconstruction with any single-pixel imaging scheme. For example, illumination with speckle-like patterns which lack the statistical properties associated with speckle does not allow accurate reconstruction with conventional methods, but frame theory-inspired analysis allows production of high-contrast, diffraction-limited images. Even for schemes where reconstruction methods exist, the theory can improve contrast, accuracy and resolution. Frame theory-motivated reconstruction from simulated ghost imaging data results in markedly improved contrast, and resolution. This analysis makes viable new single-pixel techniques which lack intuitive reconstruction strategies, and tuning of imaging properties such as noise for specific applications.

Conference Presentation

Citation Download Citation

Robert J. Stokoe, Patrick A. Stockton, Ali Pezeshki, and Randy A. Bartels "Computational improvement in single-pixel imaging contrast and resolution (Conference Presentation)", Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108830K (4 March 2019); https://doi.org/10.1117/12.2506903

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KEYWORDS

Image resolution

Sensors

Signal to noise ratio

Statistical analysis

Detector development

Image segmentation

Microscopy

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