Temporal statistics de-ghosting for adaptive non-uniformity correction in infrared focal plane arrays

Rossi, Alessandro; Diani, Marco; Corsini, Giovanni

doi:10.1049/el.2010.3559

Cited by 18 publications

(8 citation statements)

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“…The other method is based on statistics. 17,18 These methods usually make use of mean and variance of the previous data samples (or error samples) as a prior to examine the following data. Since in our method, the nonuniformity parameters are inherently calculated by mean and mean deviation statistics.…”

Section: Deghostingmentioning

confidence: 99%

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Scene-based nonuniformity correction method using multiscale constant statistics

Zuo

Chen

et al. 2011

Opt. Eng.

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In scene-based nonuniformity correction (NUC) methods for infrared focal plane array cameras, the statistical approaches have been well studied because of their lower computational complexity. However, when the assumptions imposed by statistical algorithms are violated, their performance is poor. Moreover, many of these techniques, like the global constant statistics method, usually need tens of thousands of image frames to obtain a good NUC result. In this paper, we introduce a new statistical NUC method called the multiscale constant statistics (MSCS). The MSCS statically considers that the spatial scale of the temporal constant distribution expands over time. Under the assumption that the nonuniformity is distributed in a higher spatial frequency domain, the spatial range for gain and offset estimates gradually expands to guarantee fast compensation for nonuniformity. Furthermore, an exponential window and a tolerance interval for the acquired data are introduced to capture the drift in nonuniformity and eliminate the ghosting artifacts. The strength of the proposed method lies in its simplicity, low computational complexity, and its good trade-off between convergence rate and correction precision. The NUC ability of the proposed method is demonstrated by using infrared video sequences with both synthetic and real nonuniformity. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). IntroductionThermal array detectors, also known as infrared focal plane arrays (IRFPA), are a rapidly developing technology and are used in a wide range of industry, medical, and military applications. However, the nonuniformity in IRFPAs, which is due to pixel-to-pixel variation in the detectors' response, can considerably degrade the quality of IR images since it results in a fixed-pattern noise (FPN) that is superimposed on the true image. 1 Therefore, nonuniformity correction (NUC), being an indispensable key step, is applied to nearly all of the IRFPA-based engineering applications. Further, what makes the problem worse is that the nonuniformity varies over time and is closely related to external conditions, 2, 3 which results in the failure of traditional reference-based NUC methods. In order to solve this problem, several scene-based nonuniformity correction (SBNUC) techniques have been recently developed.There are two main categories of SBNUC: statistical methods 4-7 and registration-based methods. 8-10 Compared with registration-based methods, statistical approaches have been well studied because of their relatively lower computational complexity, smaller storage demands, and better realtime performance. The most well-known statistical method relies on the global constant statistic (GCS) assumption, 4, 5 which states that the statistics of the observed scene become constant over time. This assumption requires that each detector in the array spend an equal amount of time observing a wide range of irradiance values. So it usually needs

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Section: Deghostingmentioning

confidence: 99%

“…We adopt a temporal statistics deghosting method similar to the method in Ref. 17. We use the temporal statistics of previous frames to decide whether the current pixel value will be used to compute the correction coefficients.…”

Section: Deghostingmentioning

confidence: 99%

Scene-based nonuniformity correction method using multiscale constant statistics

Zuo

Chen

et al. 2011

Opt. Eng.

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“…In general, SBNUC schemes can be broadly divided into two categories: constant statistics (CS) methods [4][5][6] and least mean square (LMS) methods. [7][8][9][10] The original CS method assumes that the temporal mean and standard deviation of each pixel are constant over time and space. 5 The performance of the original CS method is reliable as long as the assumption is valid.…”

Section: Introductionmentioning

confidence: 99%

“…An approach proposed in Ref. 10 updates correction parameters only when sufficient change occurs between consecutive images.…”

Section: Introductionmentioning

confidence: 99%

Regularization approach to scene-based nonuniformity correction

Kim

et al. 2014

Opt. Eng

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Abstract. Various scene-based nonuniformity correction (SBNUC) methods have been proposed to diminish the residual nonuniformity (RNU) of the infrared focal plane array (IRFPA) sensors. Most existing SBNUC techniques require a relatively large number of image frames to reduce the RNU. In some applications, however, there is not enough time for capturing a large number of image frames prior to the camera operation, or only several image frames are available to users. A new scene-based approach that can correct the RNU using only several image frames is proposed. The proposed method formulates the SBNUC process as an energy minimization problem. In the proposed energy function, we introduce regularization terms for the parameter regarding the responsivity of the IRFPA as well as for the true scene irradiance. Correction results are obtained by minimizing the energy function using a numerical technique. Experimental results demonstrate the effectiveness of the proposed method. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Scene-based algorithms are generally identified by two main approaches: namely, statistical methods [2][3][4][5][6][7] and registration based methods [8][9][10][11]. Statistical techniques typically exploit some spaciotemporal assumptions on the irradiance observed by each detector.…”

Section: Introductionmentioning

confidence: 99%

Improved interframe registration based nonuniformity correction for focal plane arrays

Zuo

Chen

et al. 2012

Infrared Physics & Technology

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Temporal statistics de-ghosting for adaptive non-uniformity correction in infrared focal plane arrays

Cited by 18 publications

References 1 publication

Scene-based nonuniformity correction method using multiscale constant statistics

Scene-based nonuniformity correction method using multiscale constant statistics

Regularization approach to scene-based nonuniformity correction

Improved interframe registration based nonuniformity correction for focal plane arrays

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