Proceedings Article10.1117/12.643203
Thin digital imaging systems using focal plane coding
Andrew D. Portnoy,Nikos P. Pitsianis,David J. Brady,Jungpeng Guo,Michael A. Fiddy,Michael R. Feldman,Robert Te Kolste +6 more
TL;DR: The use of focal plane coding to produce nondegenerate data between subapertures of an imaging system to reconstruct higher spatial frequencies than a conventional coarsely sampling focal plane is shown.
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Abstract: With this work we show the use of focal plane coding to produce nondegenerate data between subapertures of an imaging system. Subaperture data is integrated to form a single high resolution image. Multiple apertures generate multiple copies of a scene on the detector plane. Placed in the image plane, the focal plane mask applies a unique code to each of these sub-images. Within each sub-image, each pixel is masked so that light from only certain optical pixels reaches the detector. Thus, each sub-image measures a different linear combination of optical pixels. Image reconstruction is achieved by inversion of the transformation performed by the imaging system. Registered detector pixels in each sub-image represent the magnitude of the projection of the same optical information onto different sampling vectors. Without a coding element, the imaging system would be limited by the spatial frequency response of the electronic detector pixel. The small mask features allow the imager to broaden this response and reconstruct higher spatial frequencies than a conventional coarsely sampling focal plane.
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Citations
Compressive imaging sensors
Nikos P. Pitsianis,David J. Brady,Andrew D. Portnoy,Xiaobai Sun,Thomas J. Suleski,Michael A. Fiddy,Michael R. Feldman,Robert D. TeKolste +7 more
- 18 May 2006
TL;DR: This paper describes a compressive sensing strategy developed under the Compressive Optical MONTAGE Photography Initiative and demonstrates that the system can achieve up to 50% compression with conventional benchmarking images.
Superresolution Imaging—Revisited
TL;DR: Methods for synthesizing and recovering signal features below the diffraction limit are revisited and interpreted as instances of a single unifying principle that allows one to relate various superresolution imaging methods, as well as superoscillations, and generalized and compressive sampling schemes to one another.
38
Multichannel sampling schemes for optical imaging systems
TL;DR: A framework of focal-plane coding schemes for multichannel sampling in optical systems is introduced and an objective is to develop an ultrathin imager without compromising image resolution.
23
Compressive sampling strategies for integrated microspectrometers
David J. Brady,Michael E. Gehm,Nikos P. Pitsianis,Xiaobai Sun +3 more
- 05 May 2006
TL;DR: In this article, the authors consider compressive sensing in the context of optical spectroscopy and compare the fidelity of sampling and inference strategies over a family of spectral signals, and describe measurement constraints specific to optical spectrometers, inference models based on physical or statistical characteristics of the signals.
Exposure-Programmable CMOS Pixel With Selective Charge Storage and Code Memory for Computational Imaging
TL;DR: The proposed pixel design paves a promising path to achieve on-chip temporal-spatial exposure encoding directly on the sensor focal plane for computational imaging.
13
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TL;DR: A compact image-capturing system called TOMBO (an acronym for thin observation module by bound optics) is presented in which the compound-eye imaging system is utilized to achieve a thin optical configuration.
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David J. Brady,Michael R. Feldman,Nikos P. Pitsianis,Junpeng Guo,Andrew D. Portnoy,Michael A. Fiddy +5 more
TL;DR: F focal plane coding enables a reduction in the transverse aperture size, physical layer compression of multispectral and hyperspectral data cubes, joint optical and electronic optimization for 3D sensing, tracking, feature-specific imaging and conformal array deployment.