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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 52, Iss. 10 — Apr. 1, 2013
  • pp: COSI1–COSI2
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Computational Optical Sensing and Imaging: Introduction to Feature Issue

David R. Gerwe, Andrew Harvey, and Michael E. Gehm  »View Author Affiliations


Applied Optics, Vol. 52, Issue 10, pp. COSI1-COSI2 (2013)


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Abstract

The 2012 Computational Optical Sensing and Imaging (COSI) conference of the Optical Society of America was one of six colocated meetings composing the Imaging and Applied Optics Congress held in Monterey, California, 24–28 June. COSI, together with the Imaging Systems and Applications, Optical Sensors, Applied Industrial Optics, and Optical Remote Sensing of the Environment conferences, brought together a diverse group of scientists and engineers sharing a common interest in measuring and processing of information carried by optical fields. This special feature includes several papers based on presentations given at the 2012 COSI conference as well as independent contributions, which together highlight several important trends.

© 2013 Optical Society of America

Optical sensing and imaging systems are central to many fields of science and are ubiquitous across a variety of domains including industry, medicine, defense, commerce, art, and personal recreation. There is continual motivation to create smaller, lower cost, and more capable systems. This pressure drives a high rate of innovation, with many of the novel systems frequently relying on a high level of synergy between the optical measurement–hardware design and signal processing–computational optical sensing and imaging (COSI). Major themes of this subfield include the following:
  • Measuring projections or transforms of the underlying signal to avoid the limitations of conventional approaches—such as in synthetic aperture imaging, which avoids the resolution limit associated with the size of the physical receiver aperture, or holography and correlography methods that entirely eliminate the lens system.
  • Use of a priori knowledge regarding signal properties to achieve improved sensing performance and enable novel sensing methods, such as in the recent developments in compressed sensing.
  • Efficient methods for obtaining information about higher-dimensional optical fields, for example, 3D and light-field sensing.
  • Advanced signal recovery methods for inferring the underlying signal from measurements acquired in complicated transform domains.
  • Use of auxiliary measurements (i.e., measurement diversity) to overcome a variety of performance-limiting signal degradations, such as motion blur, wavefront error, and detector undersampling.

In all these cases, direct incorporation of signal processing into the system design is required to reconstruct the image or embedded signal in a form and dimensionality that best conveys the information of interest to an observer. High rates of advancement in source, detector, and computational technology are combining to rapidly advance the field of COSI.

One of the earliest developments in computational imaging was by Hauesler in 1972, who, in the absence of powerful digital computation, used coherent optical computation for image recovery to yield an enhanced depth of field from multiple defocused images. In this issue Casteneda et al. present an analysis of a modern variation on this technique in which digital image recovery is possible. The plenoptic camera has recently demonstrated the possibility of recording 4D light fields that enable the possibility of postdetection processing to intelligently synthesize aberrations, particularly defocus. Here, Muenzel and Fleischer describe an improvement of the generation of 4D light fields for 3D imaging. Image reconstruction is a notoriously ill-posed problem, typically resolved by regularization. Dillon and Fainman take the alternative approach of calculating the bounds of the image intensity distribution that is compatible with measurements and constraints. The use of compressed sensing techniques for hyperspectral imaging using coded measurement to simultaneously collect the data on a single 2D FPA is a current area of high attention. In this issue, papers by August et al. and Arguello et al. present coding approaches that decrease calibration sensitivities and enable optimization of the ratio of the compression level between the spatial and spectral domains. Vera et al. present exciting progress in the use nonstationary priors for deconvolution of blurred images that adaptively adjust the regularization to the image content. Lu et al. present a novel use of phase retrieval involving propagation through photorefractive materials and adjustments to the strength of the non-linearity to provide measurement diversity . In the area of extended depth of field microscopy using point spread function engineering, Zahreddine et al. demonstrate nonlinear processing filtering techniques to improve the SNR of the reconstructed image.

These and other papers in this feature issue highlight recent advances in the field of COSI, and represent but a small subset of the wealth of new progress within this diverse field. We hope this collection will inspire further collaboration and progress in this exciting area.

OCIS Codes
(110.0110) Imaging systems : Imaging systems
(110.1758) Imaging systems : Computational imaging

History
Original Manuscript: March 22, 2013
Published: April 1, 2013

Citation
David R. Gerwe, Andrew Harvey, and Michael E. Gehm, "Computational Optical Sensing and Imaging: Introduction to Feature Issue," Appl. Opt. 52, COSI1-COSI2 (2013)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-52-10-COSI1


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