Dithered scan detector compensation

O'Neil, William F.; Accetta, Joseph S.; Cantella, M. J.

doi:10.1117/12.2300232

Cited by 15 publications

(9 citation statements)

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“…The projections of the images, d1, i = 1,2, in the horizontal and vertical directions, respectively, are defined by d7(y) = >dj(X,y) (5) and d(x) = d(x,y) (6) The shifts are estimated from these projections by locating the peaks of the estimated one-dimensional crosscorrelation functions of the vector projections. In particular, we define the vertical and horizontal vector crosscorrelation functions respectively as, P(z) = >d(x)4(z+x) (7) and P(w) = d (y)d(w + y). (8) Using these statistics, the shifts are estimated independently as, Y2 maxP(z), (9) and in the other dimension, fl2 maxP(w).…”

Section: Projection-based Shift Estimatormentioning

confidence: 99%

<title>Efficient projection-based technique for registering images in the presence of fixed-pattern noise</title>

Cain¹,

Hayat²,

Armstrong³

2000

Hybrid Image and Signal Processing VII

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The problem of registering two successive video frames has traditionally been addressed with the two-dimensional cross-correlation shift estimator. In this paper, a computationally efficient method of image registration is investigated that can achieve improved registration performance over the two-dimensional cross-correlator in the presence of both fixed-pattern and temporal noise. This is accomplished by transforming both the current frame and the previous frame into two vector projections formed by accumulating pixel values across the rows and the columns of the array. The two-dimensional cross-correlator requires three two-dimensional Fourier transforms at the size of the image. In order to avoid the use of two-dimensional Fourier transforms for large arrays, other shift estimation procedures have been developed that rely only on gradients between the two frames to infer the inter-frame shifts. Gradientbased techniques exhibit degraded performance in comparison to the two-dimensional cross-correlator since the gradient operation amplifies noise. The projection-based estimator alleviates the computational burden of estimating shifts while improving the performance relative to the two-dimensional cross-correlation shift estimator. In order to demonstrate the noise rejection capability of the projection-based shift estimator, a figure of merit is developed that reflects the signal-to-noise ratio for the two different shift estimation procedures. The relative performance of the two-dimensional cross-correlation shift estimator and the projection-based shift estimator can be compared through their associated figures of merit. These two methods are also compared through computer simulation.

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Section: Projection-based Shift Estimatormentioning

confidence: 99%

<title>Efficient projection-based technique for registering images in the presence of fixed-pattern noise</title>

Cain¹,

Hayat²,

Armstrong³

2000

Hybrid Image and Signal Processing VII

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“…There are many different types of scene-based techniques. Some are statistical in nature, such as those developed by Narendra et al, 3,4 Harris et al, 5,6 Chiang et al, 7 and more recently by Hayat et al 8 and Torres et al 9,10 Motion-based algorithms include those by O'Neil et al 11,12 and Hardie et al 13 Scene-based techniques are typically used in applications where radiometric accuracy is not required, as they usually provide cosmetic image enhancement while compromising radiometric accuracy. Despite considerable advancement in computer-processing speed in recent years, many scene-based techniques are computationally intensive and cannot be implemented in real time.…”

Section: Introductionmentioning

confidence: 99%

Radiometrically calibrated scene-based nonuniformity correction for infrared array sensors

Ratliff¹,

Hayat²,

Tyo³

2003

SPIE Proceedings

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This paper describes how a limited form of black-body-based calibration can be integrated into a recently developed algebraic scene-based algorithm for nonuniformity correction (NUC) in focal-plane arrays. The result of this integration is a scene-based NUC algorithm that is radiometrically accurate. By calibrating only those detectors that are on the array perimeter and relying on the scene-based algorithm to calibrate the interior detectors, using the perimeter detectors as a reference, radiometric accuracy can be achieved without disturbing the functionality of interior array elements. What makes this possible is the fact that the scene-based NUC algorithm used here is algebraic in nature and does not rely on any statistical assumptions on the scene irradiance in the image sequence. The algorithm utilizes knowledge of inter-frame motion to "lock" the biases of the interior array elements to those on the boundary. Notably, this can be achieved regardless of the spatial diversity in the scene and with, typically, a minimal number of frames in an image sequence. The performance of the technique is demonstrated using real infrared data.

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“…The technique was first proposed by O'Neil 15,16 and further developed by Ratliff et al 8 The residual term after subtraction provides a measure of the NU remaining in the image. This is a technique whereby we shift one camera frame to align with another and subtract the two frames so that the scene is canceled out in processing.…”

Section: Introductionmentioning

confidence: 99%

Feedback-integrated scene cancellation scene-based nonuniformity correction algorithm

Black

Tyo

2014

J. Electron. Imaging

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A new registration-based scene-based nonuniformity correction (SBNUC) technique called the feedback-integrated scene cancellation (FiSC) method is introduced, which demonstrates an ability to correct both high-and low-spatial frequency nonuniformity (NU) in infrared focal plane arrays. The theory of scene-cancellation is further developed to include a referencing mechanism that allows spatially correlated NU to be corrected, and a practical method of application is developed. The algorithm is suitable for implementation in a real-time processing environment such as a digital signal processor. A new metric called normalized root mean-squared error for quantifying SBNUC performance is introduced and applied. When applied to real data from a cooled HgTeCd focal plane, the FiSC algorithm outperforms other SBNUC algorithms considered when provided with accurate frame-to-frame image registration. An SBNUC simulation is described and applied to several SBNUC algorithms. When the most realistic case including both high-and low-spatial frequency NU is simulated, the FiSC algorithm outperforms all others tested. Black and Tyo: Feedback-integrated scene cancellation scene-based nonuniformity correction algorithm Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms Black and Tyo: Feedback-integrated scene cancellation scene-based nonuniformity correction algorithm Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms Black and Tyo: Feedback-integrated scene cancellation scene-based nonuniformity correction algorithm Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms J. Scott Tyo received his BSE, MSE, and PhD degrees in electrical engineering from the University of Pennsylvania. From Black and Tyo: Feedback-integrated scene cancellation scene-based nonuniformity correction algorithm Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms

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Dithered scan detector compensation

Cited by 15 publications

References 0 publications

<title>Efficient projection-based technique for registering images in the presence of fixed-pattern noise</title>

<title>Efficient projection-based technique for registering images in the presence of fixed-pattern noise</title>

Radiometrically calibrated scene-based nonuniformity correction for infrared array sensors

Feedback-integrated scene cancellation scene-based nonuniformity correction algorithm

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