Proposed performance measures for imaging systems with discrete detector arrays

Lettington, Alan H.; Dunn, D.B.; Fairhurst, Alison M.; Fang, Yi-Chin

doi:10.1080/09500340108235159

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Lettington et al pointed out that many imaging systems have an IRF that approximates to a Gaussian function [22]. The shape of IRF can be assumed as a Gaussianlike function [20].…”

Section: Parametric Instrument Response Functionmentioning

confidence: 99%

Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images

Yan

Liu

Chen

et al. 2013

Journal of Modern Optics

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2013) Parametric semi-blind deconvolution algorithm with Huber-Markov regularization for passive millimeter-wave images, Journal of Modern Optics, 60:12, 970-982, Passive millimeter-wave (PMMW) images often suffer common problems of noise and blurring. A new method is proposed to estimate the instrument response function (IRF) and desired image simultaneously. The proposed variational model integrates the adaptive weight data term, image smooth term, and IRF smooth term. The major novelty of this work is that Huber-Markov regularization is adopted for PMMW image restoration, which can preserve structural details as well as suppress noise effectively. The IRF is parametrically formulated as a Gaussian-shaped function based on experimental measurements through the utilized PMMW imaging system. The alternation minimization iterative method is applied to achieve the IRF width and desired image. Comparative experimental results with some real PMMW images reveal that the proposed approach can effectively suppress noise, reduce ringing artifacts, and improve the spatial resolution.

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“…Lettington et al pointed out that many imaging systems have an IRF that approximates to a Gaussian function [22]. The shape of IRF can be assumed as a Gaussianlike function [20].…”

Section: Parametric Instrument Response Functionmentioning

confidence: 99%

Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images

Yan

Liu

Chen

et al. 2013

Journal of Modern Optics

View full text Add to dashboard Cite

show abstract

“…This however can be avoided by terminating the restoration once the known scene edge distribution has been achieved. The method is stable in the presence of noise and by sharpening edges and suppressing ringing it can increase the signal-to-noise ratio, which in turn can increase the probability of observers recognizing objects in the scene 19 . The previous one-dimensional algorithm 3 was adapted from Fuderer's super-resolution work 18 and produced good results for one-dimensional images.…”

Section: Lorentzian Restoration Techniquementioning

confidence: 99%

Constrained image restoration applied to passive millimeter-wave images

Lettington

Alexander

2004

SPIE Proceedings

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Passive millimeter-wave imaging has excellent all weather capability but is severely diffraction limited and requires large apertures to give adequate spatial resolution. Linear restoration can enhance the resolution by a factor of two, while under favorable conditions non-linear restoration can enhance it by factors of four. The amount of enhancement possible is generally limited by the amount of noise present in the original observed image. Preprocessing can reduce the effect of this noise. In many non-linear restoration techniques the amount of high spatial frequency introduced into the restored image is uncontrolled. This problem has been overcome through the use of the Lorentzian algorithm, which imposes a statistical constraint on the distribution of gradients within the restored image. Another way of applying a constraint is to selectively restore an image. The high spatial frequency content of an image exists largely at edges and sharp features and needs to be restored, while the smoother background between features contains fewer high frequencies and needs less restoration. Adaptive non-linear restoration techniques have been investigated whereby the amount of restoration applied to an image is a function of the first and second derivative of the image intensity. Images are presented to demonstrate the effectiveness of these methods.

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Prediction of the Thermal Imaging Minimum Resolvable (Circle) Temperature Difference with Neural Network Application

Fang

2008

IEEE Trans. Pattern Anal. Mach. Intell.

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Thermal imaging is an important technology in both national defense and the private sector. An advantage of thermal imaging is its ability to be deployed while fully engaged in duties, not limited by weather or the brightness of indoor or outdoor conditions. However, in an outdoor environment, many factors, including atmospheric decay, target shape, great distance, fog, temperature out of range and diffraction limits can lead to bad image formation, which directly affects the accuracy of object recognition. The visual characteristics of the human eye mean that it has a much better capacity for picture recognition under normal conditions than artificial intelligence does. However, conditions of interference significantly reduce this capacity for picture recognition for instance, fatigue impairs human eyesight. Hence, psychological and physiological factors can affect the result when the human eye is adopted to measure MRTD (minimum resolvable temperature difference) and MRCTD (minimum resolvable circle temperature difference). This study explores thermal imaging recognition, and presents a method for effectively choosing the characteristic values and processing the images fully. Neural network technology is successfully applied to recognize thermal imaging and predict MRTD and MRCTD (Appendix A), exceeding thermal imaging recognition under fatigue and the limits of the human eye.

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Proposed performance measures for imaging systems with discrete detector arrays

Cited by 4 publications

References 9 publications

Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images

Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images

Constrained image restoration applied to passive millimeter-wave images

Prediction of the Thermal Imaging Minimum Resolvable (Circle) Temperature Difference with Neural Network Application

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