NVThermIP modeling of super-resolution algorithms

Jacobs, Eddie L.; Driggers, Ronald G.; Young, S. Susan; Krapels, Keith A.; Tener, Gene; Park, Jennifer

doi:10.1117/12.604900

Cited by 9 publications

(8 citation statements)

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“…The effect of sensor motion on human performance is not yet modeled in NVThermIP. The effect of super-resolution is modeled, based on an optimum use of the additional sampling (Jacobs et al, 2005) 11 . The used orientation is 45°(L+R), which amounts to an average size of 0.255 and average dT of 2.02 K.…”

Section: Results 2: Signal Processingmentioning

confidence: 99%

“…TA models [9][10][11][12][13][14][15][16] such as the TOD model, NVThermIP and TRM3 need to be extended and validated for these largely different target sets, sensor properties, image dynamics and processing techniques, but currently the amount of empirical data is limited and often only addresses one of the above-mentioned differences at the same time. For instance, a large effort of the US research laboratory is directed towards extension of the current models for different target sets such as small arms 17,18 , clothing 19 , paramilitary and civilian vehicles 20 , small boats 21 , or even faces 22 in different spectral regions.…”

Section: Introductionmentioning

confidence: 99%

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Target acquisition performance: effects of target aspect angle, dynamic imaging, and signal processing

Beintema¹,

Bijl²,

Hogervorst³

et al. 2008

SPIE Proceedings

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In an extensive Target Acquisition (TA) performance study, we recorded static and dynamic imagery of a set of military and civilian two-handheld objects at a range of distances and aspect angles with an under-sampled uncooled thermal imager. Next, we applied signal processing techniques including DSR (Dynamic Super Resolution) and LACE (Local Adaptive Contrast Enhancement) to the imagery. In a perception experiment, we determined identification (ID) and threat/non-threat discrimination performance as a function of target range for a variety of conditions. The experiment was performed to validate and extend current TA models. In addition, range predictions were performed with two TA models: the TOD model and NVThermIP. The results of the study are: i) target orientation has a strong effect on performance, ii) the effect of target orientation is well predicted by the two TA models, iii) absolute identification range is close the range predicted with the two models using the recommended criteria for two-handheld objects, iv) there was no positive effect of sensor motion on performance, and this was against the expectations based on earlier studies, v) the benefit of DSR was smaller than expected on the basis of the model predictions, and vi) performance with LACE was similar to performance on an image optimized manually, indicating that LACE can be used to optimize the contrast automatically. The relatively poor results with motion and DSR are probably due to motion smear induced by a higher camera speed than used in earlier studies. Camera motion magnitude and smear are not yet implemented in TA models.

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Section: Results 2: Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Target acquisition performance: effects of target aspect angle, dynamic imaging, and signal processing

Beintema¹,

Bijl²,

Hogervorst³

et al. 2008

SPIE Proceedings

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“…Dynamic signal processing techniques such as super-resolution reconstruction (SR) or scene-based non-uniformity correction (SBNUC) and many others have been widely reported in the literature 1,2,3,8 . These techniques make use of motion in the image in order to improve pixel resolution and/or to reduce temporal noise (SR) in an undersampled system or to improve contrast sensitivity (reduce the fixed pattern noise in real time) by comparing the responses from one pixel to the other using the scene (SBNUC).…”

Section: Dynamic Signal Processing Techniquesmentioning

confidence: 99%

“…This makes the MRT essentially not useful for dynamic imaging, since we are interested in the effects of dynamic imaging and enhancement techniques which are mainly applied to undersampled imagers. The US NVESD range performance model NVThermIP 8 calculates the MRT but includes a warning that this MRT is only for comparison with a lab test and should not be used for range predictions. There are several other problems with the MRT.…”

Section: Mrtmentioning

confidence: 99%

“…We perform a theoretical analysis to the test methods, and those methods that pass this analysis are applied experimentally to static and dynamic conditions with and without processing at high, medium and low thermal contrast. Finally, the experimental results are compared to predictions made with the Target Acquisition models NVThermIP 8 and TRM3 6 .…”

Section: Introductionmentioning

confidence: 99%

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Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Bijl¹,

Schutte²,

Hogervorst³

2006

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVII

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Current end-to-end sensor performance measures such as the TOD, MRT, DMRT and MTDP were developed to describe Target Acquisition performance for static imaging. Recent developments in sensor technology (e.g. microscan) and image enhancement techniques (e.g. Super Resolution and Scene-Based Non-Uniformity Correction) require that a sensor performance measure can be applied to dynamic imaging as well. We evaluated the above-mentioned measures using static, dynamic (moving) and different types of enhanced imagery of thermal 4-bar and triangle tests patterns. Both theoretical and empirical evidence is provided that the bar-pattern based methods are not suited for dynamic imaging. On the other hand, the TOD method can be applied easily without adaptation to any of the abovementioned conditions, and the resulting TOD data are in correspondence with the expectations. We conclude that the TOD is the only current end-to-end measure that is able to quantify sensor performance for dynamic imaging and dynamic image enhancement techniques.

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