An algebraic algorithm for nonuniformity correction in focal-plane arrays

Ratliff, Bradley M.; Hayat, Majeed M.; Hardie, Russell C.

doi:10.1364/josaa.19.001737

Cited by 117 publications

(73 citation statements)

References 13 publications

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“…The most widely used algorithms [4,2,3,10] are either based on making each detector output similar to those of the surrounding detectors in the array, or based on statistical analysis. Some more advanced methods depending on image motion estimation was also found [7,8].…”

Section: Previous Workmentioning

confidence: 91%

“…It has been shown by Ratliff et al [7] that for pure horizontal or vertical sub-pixel translations, the responses of two detectors at unity distance can be accurately related through extrapolation. An elegant nonuniformity correction method for offset-only calibration (3.2) can be derived that unifies the offset terms to one unknown but global value.…”

Section: Crossing Pathmentioning

confidence: 99%

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Scene-based correction of image sensor deficiencies

2003

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This thesis describes and evaluates a number of algorithms for reducing fixed pattern noise in image sequences by analyzing the observed scene. Fixed pattern noise is the dominant noise component for many infrared detector systems, perceived as a superimposed pattern that is approximately constant for all image frames.Primarily, methods based on estimation of the movement between individual image frames are studied. Using scene-matching techniques, global motion between frames can be successfully registered with sub-pixel accuracy. This allows each scene pixel to be traced along a path of individual detector elements. Assuming a static scene, differences in pixel intensities are caused by fixed pattern noise that can be estimated and removed.The algorithms have been tested by using real image data from existing infrared imaging systems with good results. The tests include both a two-dimensional focal plane array detector and a linear scanning one-dimensional detector, in different scene conditions.

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Section: Previous Workmentioning

confidence: 91%

Section: Crossing Pathmentioning

confidence: 99%

Scene-based correction of image sensor deficiencies

2003

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“…Hayat et al 14 developed a statistical algorithm that relies on a key assumption that, in time, all detectors in the array are exposed to the same range of irradiance, which is further modeled by a uniformly distributed random variable with a constant range. Recently, Ratliff et al 15 developed an algebraic (nonstatistical) scene-based NUC technique that does not rely on any statistical or scene-diversity assumptions about the scene temperature. The algorithm utilizes estimates of interframe subpixel motion and a linear interpolation model for image motion to unify the biases of the detectors.…”

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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“…In [28] motion based non-uniformity correction methods are explained for Division of Focal Plane (DoFP) polarimeters. Ratliff et al [21] particularly, have proposed an algebraic method based on motion vector estimation. Such techniques provide accurate robustness however, are computationally heavy and no longer without a doubt suitable for real-time correction.…”

Section: Related Workmentioning

confidence: 99%

Scene based Non-uniformity Correction for Optical Remote Sensing Imagery

Gopan¹,

Venkateswarlu²,

Nair³

et al. 2017

IJIGSP

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Abstract-In this work, we propose and evaluate different scene based methods for non-uniformity corrections for optical remote sensing data sets. These methods can be used to correct or refine the existing radiometric calibrations, thereby improving the image quality. The performance of each algorithm against different datasets are analyzed and a quantitative comparison of different quality parameters viz. entropy, correlation coefficient, signal to noise ratio, peak signal to noise ratio and structural similarity index are carried out to recommend the best method for each scene. For a given data set, the selected method depends on the severity, type of terrain it covered, etc.

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An algebraic algorithm for nonuniformity correction in focal-plane arrays

Cited by 117 publications

References 13 publications

Scene-based correction of image sensor deficiencies

Scene-based correction of image sensor deficiencies

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

Scene based Non-uniformity Correction for Optical Remote Sensing Imagery

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