Laboratory measurement of sampled infrared imaging system performance

Driggers, Ronald G.

doi:10.1117/1.602249

Cited by 32 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[19][20][21][22] However, earlier MRT models did not accurately predict the change in MRT values as sample phase varied.…”

Section: Discussionmentioning

confidence: 99%

Predicting the effect of gain, level, and sampling on minimum resolvable temperature measurements

Vollmerhausen¹

2009

Opt. Eng

View full text Add to dashboard Cite

Abstract. We describe a model to predict the minimum resolvable temperature ͑MRT͒ performance of thermal imagers. Although MRT is a common measurement, it is difficult to achieve consistent results. The operator is permitted but not mandated to change gain, level, and sample phasing for each bar pattern viewed. Changing the sensor control settings affects the resulting MRT. However, the state of the imager is not recorded along with the temperature data. The model predicts the effect of gain, level, and sample phase on MRT results. Model predictions are compared to measurements to demonstrate theory validity. The comparisons also demonstrate the variability that results from ignoring the sensor state when reporting MRT. © 2009 Society of Photo-Optical Instrumentation Engineers.

show abstract

“…[19][20][21][22] However, earlier MRT models did not accurately predict the change in MRT values as sample phase varied.…”

Section: Discussionmentioning

confidence: 99%

Predicting the effect of gain, level, and sampling on minimum resolvable temperature measurements

Vollmerhausen¹

2009

Opt. Eng

View full text Add to dashboard Cite

show abstract

“…To properly characterize the system it was necessary to measure or set the following parameters: [1] is measured to determine the gain conversation from temperature in Kelvin to digital counts measured by the sensor. Although not typically measured, the transfer function of the display was also measured using a handheld photometer.…”

Section: Sensor Characterizationmentioning

confidence: 99%

“…The Minimum Resolvable Temperature Difference (MRTD or MRT) [1], [2] is a laboratory test that had performance implications for 1st and 2nd generation FLIRs (scanned systems). The MRT is considered to be a visual acuity evaluation of sensor performance, MRT is measurable in the laboratory, and can be related to field performance.…”

Section: Introductionmentioning

confidence: 99%

Modeling MRT for well-characterized thermal imagers

2011

View full text Add to dashboard Cite

Predicting an accurate Minimum Resolvable Temperature Difference (MRTD) for a thermal imaging system is often hindered by inaccurate measurements of system gain and display characteristics. Variations in these terms are often blamed for poor agreement between model predictions and measured MRTD. By averaging over repeated human measurements, and carefully recording all system parameters affecting image quality, it should be possible to make an accurate prediction of MRTD performance for any resolvable frequency. Utilizing the latest NVESD performance models with updates for noise, apparent target angle, and human vision, predicted MRT are compared with measured curves. We present results for one well characterized mid-wave thermal staring system. *This document is approved for public release; distribution is unlimited. BACKGROUNDThe Minimum Resolvable Temperature Difference (MRTD or MRT) [1],[2] is a laboratory test that had performance implications for 1st and 2nd generation FLIRs (scanned systems). The MRT is considered to be a visual acuity evaluation of sensor performance, MRT is measurable in the laboratory, and can be related to field performance. Embedded in test results are a characterization of the thermal sensitivity and resolution of the sensor and the ability of a human observer to use the sensor to discriminate objects.It was once extremely important to model and measure the MRT performance for a given sensor. The utility of this concept was that the detection, recognition and identification criteria for a thermal task were related to the spatial frequency of a just visible four bar pattern (comparable to Johnson's criteria of cycles on target) as predicted either by theory or measured in the laboratory. The MRT equation, in the 1975 NVL Static Performance Model and its successors FLIR90/92 and NVTHERM, predicts ∆T for the recognition of 4-bar patterns as a function of spatial frequency. The modeled MRT equation contains sensor, target, and observer characteristics. These include detector noise, sensor component Modulation Transfer Functions (MTFs), processing electronics, display characteristics, and observer eye/brain models.Due to the sampling limitations of undersampled imaging systems, along with a myriad of other sensor effects that were not encapsulated in the MRT equation (e.g., digital effects, sensor boost, LACE, LAP, super-resolution), a new model needed to be developed that was no longer tied to a MRT equation. [3], [4] The new models NVThermIP and NV-IPM can utilize laboratory measurements as inputs, but they rely only on objective measurements such as Modulation Transfer Function and 3D Noise to determine thermal performance predictions.Still, the laboratory MRT measurement has utility in determining possible system level degradations, not easily revealed through objective laboratory measurements. These could include platform vibration, non-optimized displays, eyepiece effects, data resampling, human viewing factors, and other degradations not included in the performance models. If all ...

show abstract

“…Approximating the 3D power spectral density with the 3D noise model gives seven variances that can be loosely correlated with some underlying imager parameters. The spatial noise terms can be indicative of poor nonuniformity correction (NUC), while a high variance of the 1D temporal processes can be related to flicker [3].…”

Section: Introductionmentioning

confidence: 99%

Spatially resolved 3D noise

et al. 2016

View full text Add to dashboard Cite

When evaluated with a spatially uniform irradiance, an imaging sensor exhibits both spatial and temporal variations, which can be described as a three-dimensional (3D) random process considered as noise. In the 1990s, NVESD engineers developed an approximation to the 3D power spectral density (PSD) for noise in imaging systems known as 3D noise. In this correspondence, we describe how the confidence intervals for the 3D noise measurement allows for determination of the sampling necessary to reach a desired precision. We then apply that knowledge to create a smaller cube that can be evaluated spatially across the 2D image giving the noise as a function of position. The method presented here allows for both defective pixel identification and implements the finite sampling correction matrix. In support of the reproducible research effort, the Matlab functions associated with this work can be found on the Mathworks file exchange [1].

show abstract

Laboratory measurement of sampled infrared imaging system performance

Cited by 32 publications

References 18 publications

Predicting the effect of gain, level, and sampling on minimum resolvable temperature measurements

Predicting the effect of gain, level, and sampling on minimum resolvable temperature measurements

Modeling MRT for well-characterized thermal imagers

Spatially resolved 3D noise

Contact Info

Product

Resources

About