Adaptive retina-like preprocessing for imaging detector arrays

Scribner, Dean A.; Sarkady, Kenneth A.; Kruer, M.; Caulfield, John; Hunt, J. D.; Colbert, Michael; Descour, Michael R.

doi:10.1109/icnn.1993.298856

Cited by 96 publications

(109 citation statements)

References 7 publications

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“…By adjusting the time constant of the filter, their algorithm was used to reduce the spatial noise caused by bias nonuniformity (the gain correction was performed separately). A neural-network implementation of the adaptive leastmean-square-error algorithm was also developed by Scribner et al 9,10 O'Neil, 11 Hardie et al, 12 and Hepfer et al 13 developed NUC techniques that rely on the fact that detectors that record the same scene point at different times should have the same response. For example, O'Neil uses frames of data produced by dithering the detector line of sight between consecutive frames in a known pattern.…”

Section: Introductionmentioning

confidence: 99%

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

2003

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A novel statistical approach is undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The gain and the bias of each detector are regarded as random state variables modeled by a discrete-time Gauss-Markov process. The proposed Gauss-Markov framework provides a mechanism for capturing the slow and random drift in the fixed-pattern noise as the operational conditions of the sensor vary in time. With a temporal stochastic model for each detector's gain and bias at hand, a Kalman filter is derived that uses scene data, comprising the detector's readout values sampled over a short period of time, to optimally update the detector's gain and bias estimates as these parameters drift. The proposed technique relies on a certain spatiotemporal diversity condition in the data, which is satisfied when all detectors see approximately the same range of temperatures within the periods between successive estimation epochs. The performance of the proposed technique is thoroughly studied, and its utility in mitigating fixed-pattern noise is demonstrated with both real infrared and simulated imagery.

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Section: Introductionmentioning

confidence: 99%

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

2003

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“…Nonuniformity correction can be approached using two strategies: apply a uniform reference image to the static imager and ensure that all pixel outputs are equal [1], or drift natural scenes across the imager where each pixel subtracts its output from its spatially low-pass filtered output to derive an error signal [6]. The former is referred to as static nonuniformity correction (SNUC) and the latter as scene-based nonuniformity correction (SBNUC).…”

Section: Adaptive Nonuniformity Correctionmentioning

confidence: 99%

Floating-gate adaptation for focal-plane online nonuniformity correction

Cohen

Cauwenberghs

2001

IEEE Trans. Circuits Syst. II

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Abstract-Stochastic adaptive algorithms are investigated for online correction of spatial nonuniformity in random-access addressable imaging systems. The adaptive architecture is implemented in analog VLSI, integrated with the photosensors on the focal plane. Random sequences of address locations selected with controlled statistics are used to adaptively equalize the intensity distribution at variable spatial scales. Through a logarithm transformation of system variables, adaptive gain correction is achieved through offset correction in the log-domain. This idea is particularly attractive for compact implementation using translinear floating-gate MOS circuits. Furthermore, the same architecture and random addressing provide for oversampled binary encoding of the image with equalized intensity histogram. The techniques apply to a variety of solid-state imagers, such as artificial retinas, active pixel sensors, and IR sensor arrays. Experimental results confirm gain correction and histogram equalization in a 64 64 pixel adaptive array integrated on a 2.2-mm 2.25-mm chip in 1.2-m CMOS technology.

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“…Currently there are two main fields in correcting the noise affecting such images (technically called non−uniformity correction): one corre− sponding to the reference−based approach, which uses inter− polation techniques; and the second to the correction based on the scene, which requires parameter estimation algo− rithms to correct image online. The first method uses black body radiators [2], which is necessary to periodically re− move the camera from normal operation to perform the cali− bration process because the parameters change over time. Instead corrections based on scene avoided the latter prob− lem, since the calibration is done online, getting a reference from the sequence of images provided by the camera algo− rithms for non−uniformity correction, obviating the use of radiators black body of high−cost.…”

Section: Introductionmentioning

confidence: 99%

Acceleration algorithm for constant-statistics method applied to the nonuniformity correction of infrared sequences

Chavez

Vicencio

2015

Opto-Electronics Review

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Non−uniformity noise, it was, it is, and it will probably be one of the most non−desired attached companion of the infrared focal plane array (IRFPA) data. We present a higher order filter where the key advantage is based in its capacity to estimates the detection parameters and thus to compensate it for fixed pattern noise, as an enhancement of Constant Statistics (CS) theory. This paper shows a technique to improve the convergence in accelerated way for CS (AACS: Acceleration Algorithm for Constant Statistics). The effectiveness of this method is demonstrated by using simulated infrared video sequences and several real infrared video sequences obtained using two infrared cameras.

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Adaptive retina-like preprocessing for imaging detector arrays

Cited by 96 publications

References 7 publications

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

Floating-gate adaptation for focal-plane online nonuniformity correction

Acceleration algorithm for constant-statistics method applied to the nonuniformity correction of infrared sequences

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