Multichannel, agile, computationally enhanced camera based on the PANOPTES architecture

Milojkovic, Predrag; Gill, John; Frattin, Daniel; Coyle, Kevin; Haack, Karl; Myhr, Scot; Rajan, Dinesh; Douglas, S.C.; Papamichalis, P.; Somayaji, Manjunath; Christensen, Marc P.; Krapels, Keith A.

doi:10.1117/12.864995

Cited by 6 publications

(9 citation statements)

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“…The form of regularization in (4) (but without pre-upsampling of LR images) is first proposed in [16] for the blind MIBD problem. By rearranging R h , it can be written as R h = ||N h|| 2 2 where h =[h T 1 , . .…”

Section: B Blind Estimationmentioning

confidence: 99%

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Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution

Faramarzi

Rajan

Christensen

2013

IEEE Trans. on Image Process.

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This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life images (from a novel computational imager) confirm the robustness and effectiveness of the proposed method.

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Section: B Blind Estimationmentioning

confidence: 99%

“…constraint that has persisted since their invention [1], [2]. In contrast, computational imaging systems combine the power of digital processing with data gathered from optical elements to generate HR images.…”

mentioning

confidence: 99%

Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution

Faramarzi

Rajan

Christensen

2013

IEEE Trans. on Image Process.

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“…Marcenaro et al demonstrated a system with a stationary and an active pan-tilt-zoom camera [1], while Milojkovic et al proposed a fivechannel computationally enhanced camera system [2]. In the latter system, one channel gives a wide FOV image, while the other four channels are able to select a region of interest by the use of a steering mirror and produce an image with a larger magnification.…”

Section: Introductionmentioning

confidence: 99%

Two-channel multiresolution refocusing imaging system using a tunable liquid lens

Smeesters¹,

Belay²,

Ottevaere³

et al. 2014

Appl. Opt.

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Multichannel imaging systems currently feature refocusing capabilities only in bulky and expensive designs. Mechanical movements of the components cannot be integrated in miniaturized designs, preventing classical refocusing mechanisms. To overcome this limitation we developed, as a proof-of-concept (POC) demonstration, a compact low-cost two-channel refocusing imaging system based on a voltage-tunable liquid lens. In addition, the design can be realized with wafer-level manufacturing techniques. One channel of the imaging system enables a wide field of view (FOV) of a scene (2×40°) but with a limited angular resolution (0.078°), while the other channel gives a high angular resolution (0.0098°) image of a small region of interest but with a much narrower FOV (2×7.57°). It is this high-resolution channel that contains the tunable lens and therefore the refocusing capability. A POC demonstration of the proposed two-channel system was built and its performances were measured. Both imaging channels show good overall diffraction-limited image quality.

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“…To this end, this paper presents the development and performance results of a multiaperture, adaptive, folded-optical, computational imaging sensor prototype based on the Processing Arrays of Nyquist-limited Observations to Produce a Thin Electro-optic Sensor (PANOPTES) concept. In the PANOPTES concept, multiple subimagers-each with its own steering mechanism-work together to yield HR imagery through DSR in an adaptive framework [11][12][13][14][15]. This adaptability is realized via steering mirrors to optimally position each subimager's FOV, thus allowing dynamic, nonuniform spatial allocation of imaging resources depending on the information content of the scene [16].…”

Section: Introductionmentioning

confidence: 99%

Prototype development and field-test results of an adaptive multiresolution PANOPTES imaging architecture

et al. 2012

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The design, development, and field-test results of a visible-band, folded, multiresolution, adaptive computational imaging system based on the Processing Arrays of Nyquist-limited Observations to Produce a Thin Electro-optic Sensor (PANOPTES) concept is presented. The architectural layout that enables this imager to be adaptive is described, and the control system that ensures reliable field-of-view steering for precision and accuracy in subpixel target registration is explained. A digital superresolution algorithm introduced to obtain high-resolution imagery from field tests conducted in both nighttime and daytime imaging conditions is discussed. The digital superresolution capability of this adaptive PANOPTES architecture is demonstrated via results in which resolution enhancement by a factor of 4 over the detector Nyquist limit is achieved.

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Multichannel, agile, computationally enhanced camera based on the PANOPTES architecture

Cited by 6 publications

References 0 publications

Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution

Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution

Two-channel multiresolution refocusing imaging system using a tunable liquid lens

Prototype development and field-test results of an adaptive multiresolution PANOPTES imaging architecture

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