Electro-Optical Imaging System Performance, Sixth Edition

Holst, Gerald C.

doi:10.1117/3.2588947

Cited by 31 publications

(38 citation statements)

References 0 publications

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“…These effects can be realized by masking extended kernels of Airy disks f ij with the detector profile before average pooling. However, this option is not used in this work due to the rare use of nonsquare detectors, low signal-to-noise ratio for low fill factors, 41 and faster image generation.…”

Section: Background Overlaymentioning

confidence: 99%

Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Wegner

Keßler

2023

Opt. Eng.

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Within the last decades, a large number of techniques for contrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks, such as object recognition. In this work, the effect of contrast enhancement algorithms is evaluated by means of the triangle orientation discrimination (TOD), which is a current method for imager performance assessment. The conventional TOD approach requires observers to recognize equilateral triangles pointing in four different directions, whereas here convolutional neural network models are used for the classification task. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed over natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then, these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms, such as the contrast-limited adaptive histogram equalization or local range modification, are applied. Then accuracies of the trained models on these images are compared for different contrast enhancement algorithms. Accuracy gains for low signal-to-background ratios and sufficiently large triangles are found, whereas impairments are found for high signal-to-background ratios and small triangles. A high generalization ability of our TOD model is found from the similar accuracies for several image databases used for backgrounds. Finally, implications of replacing triangles with real target signatures when using such advanced digital signal processing algorithms are discussed. The results are a step toward the assessment of those algorithms for generic target recognition.

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Section: Background Overlaymentioning

confidence: 99%

Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Wegner

Keßler

2023

Opt. Eng.

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“…NETD is defined as the temperature difference between an extended source and the background, giving rise to a peak signal to noise ratio equal to unity at the output of the detector [1] [3] [7]. In other words, it is a measure of the smallest temperature difference that can be detected by the system above the noise floor.…”

Section: Sensitivity (Netd)mentioning

confidence: 99%

“…In summary, while NETD is not a noise parameter per se, it is closely related to the noise performance of an LWIR FPA imaging system and it is an important FOM for the system sensitivity [1]. The NETD can be computed as:…”

Section: Sensitivity (Netd)mentioning

confidence: 99%

Importance of frame rate during longwave infrared focal plane array evaluation

Santos¹,

Filho

Sá

et al. 2023

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

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Frame rate is one of the most important parameters to evaluate the performance of commercial longwave infrared focal plane arrays (LWIR FPA). However, in the literature, there are few works that investigate the frame rate effects on the behavior of the main figures of merit (FOM). In this work, the FOM of a LWIR FPA are analyzed changing the frame rate from 25 to 60. In order to verify the frame rate influence on the FOM, the FPA raw data are acquired when it is exposed to a wide area blackbody at temperatures of 25°C and 60°C. It is observed that, under certain conditions, changes in frame rate can improve the noise equivalent temperature difference (NETD) and the signal transfer function (SiTF) by 50% and 60%, respectively. Another point that deserves attention is the unpredicted emergence of a minimum point, when 45fps is employed, in the detector operability versus frame rate curve. As expected, the output voltage pixel response is also affected when the frame rate is changed. Particularly, for the 60°C temperature, variations greater than 60% on this voltage are observed.

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“…These effects can be realized by masking extended kernels of Airy disks f ij with the detector profile before average pooling. However, this option is not used in this work, due to rare use of non-square detectors, low signal-to-noise ratio for low fill factors 43 and for faster image generation.…”

Section: Degradationsmentioning

confidence: 99%

Comparison of algorithms for contrast enhancement based on TOD assessments by convolutional neural networks

Wegner

Keßler

2022

Electro-Optical and Infrared Systems: Technology and Applications XIX

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A current approach for performance assessment of imagers is triangle orientation discrimination (TOD). This approach requires observers or human visual system (HVS) models to recognize equilateral triangles pointing in four different directions. Imagers may apply embedded advanced digital signal processing (ADSP) for contrast enhancement, noise reduction, edge sharpening, etc. Unfortunately, applied methods are in general not documented and hence unknown. Within the last decades a vast amount of techniques for contrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks such as object recognition In this work different algorithms for contrast enhancement are compared in terms of TOD assessments by convolutional neural networks (CNN) as models. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed by natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms are applied, such as the contrast-limited adaptive histogram equalization or local range modification. Then accuracies of the trained models on these images are compared for different ADSP algorithms. Accuracy gains for low signal-to-background ratio and sufficiently large triangles are found, while impairments are found for high signal-to-background ratio and small triangles. Finally, implications of replacing triangles by real target signatures when using such ADSP algorithms are discussed. The results can be a step towards the assessment of those algorithms for generic target recognition.

show abstract

Electro-Optical Imaging System Performance, Sixth Edition

Cited by 31 publications

References 0 publications

Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Importance of frame rate during longwave infrared focal plane array evaluation

Comparison of algorithms for contrast enhancement based on TOD assessments by convolutional neural networks

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