A Median-Ratio Scene-Based Non-Uniformity Correction Method for Airborne Infrared Point Target Detection System

Ding, Shuai; Wang, Dejiang; Zhang, Tao

doi:10.3390/s20113273

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…In a narrow dynamic range, the detector response is linearly related to the radiated power. However, due to the large dynamic range of infrared imaging, linear relationship alone is often not enough to satisfy [5] . The relationship between P(T) and temperature T is represented by a curve [6] .…”

Section: Irfpa Detector Response Curvementioning

confidence: 99%

Research on shutter-less non-uniformity correction technology based on ambient temperature

Chen

Wang

Xie

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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Infrared imaging technology is widely used in national defense, industry, medical and other fields. High performance infrared imaging technology is highly valued by all countries in the world. However, the inhomogeneity, the inherent characteristic of infrared image, will seriously affect the real information of the image and seriously restrict the performance of infrared imaging system. In this paper, we proposed a shutter-less non-uniformity correction (SLNUC) algorithm based on ambient temperature. The SLNUC is based on the non-uniformity correction of the collected image of HgCdTE mid-wave infrared detector. The experimental results show that the SLNUC correction algorithm can adapt to the working requirements of a wide temperature range, without affecting the output of video stream, and the non-uniformity reaches 0.17% after correction, which lays a foundation for the development of new equipment.

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Section: Irfpa Detector Response Curvementioning

confidence: 99%

Research on shutter-less non-uniformity correction technology based on ambient temperature

Chen

Wang

Xie

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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“…However, the spectrometer's accuracy will be affected by a variety of error sources. The main error sources are the stray light error [19][20][21] and the detector non-uniformity error [22][23][24]. These errors make the equation AX = B unsuitable and add difficulty obtaining a solution for the equation.…”

Section: ( ) ( )mentioning

confidence: 99%

Case Study on the Fitting Method of Typical Objects

Zhang

Song

et al. 2021

Photonics

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This study proposes different fitting methods for different types of targets in the 400–900 nm wavelength range, based on convex optimization algorithms, to achieve the effect of high-precision spectral reconstruction for small space-borne spectrometers. This article first expounds on the mathematical model in the imaging process of the small spectrometer and discretizes it into an AX = B matrix equation. Second, the design basis of the filter transmittance curve is explained. Furthermore, a convex optimization algorithm is used, based on 50 filters, and appropriate constraints are added to solve the target spectrum. First, in terms of spectrum fitting, six different ground object spectra are selected, and Gaussian fitting, polynomial fitting, and Fourier fitting are used to fit the original data and analyze the best fit of each target spectrum. Then the transmittance curve of the filter is equally divided, and the corresponding AX = B discrete equation set is obtained for the specific object target, and a random error of 1% is applied to the equation set to obtain the discrete spectral value. The fitting is performed for each case to determine the best fitting method with errors. Subsequently, the transmittance curve of the filter with the detector characteristics is equally divided, and the corresponding AX = B discrete equation set is obtained for the specific object target. A random error of 1% is applied to the equation set to obtain the error. After the discrete spectral values are obtained, the fitting is performed again, and the best fitting method is determined. In order to evaluate the fitting accuracy of the original spectral data and the reconstruction accuracy of the calculated discrete spectrum, the three evaluation indicators MSE, ARE, and RE are used for evaluation. To measure the stability and accuracy of the spectral reconstruction of the fitting method more accurately, it is necessary to perform 500 cycles of calculations to determine the corresponding MSE value and further analyze the influence of the fitting method on the reconstruction accuracy. The results show that different fitting methods should be adopted for different ground targets under the error conditions. The three indicators, MSE, ARE, and RE, have reached high accuracy and strong stability. The effect of high-precision reconstruction of the target spectrum is achieved. This article provides new ideas for related scholars engaged in hyperspectral reconstruction work and promotes the development of hyperspectral technology.

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“…The pixel level non-uniformity is also called ''salt-and-pepper'' xed pattern noise (FPN), which has not been studied in perovskite X-ray detectors. Non-uniform noise seriously affects the imaging quality, reduces the signal-to-noise ratio (SNR) and the detector resolution, which is the bottleneck restricting all kinds of image sensors to reach the background limit [19][20][21][22][23][24] . We nd that the FPN values of the previous perovskite X-ray FPDs are as high as 12% 6 and 46% 4 , much higher than conventional image sensors (< 0.5% for visible light sensors 25 and < 3% for infrared sensors 26 ), and the pixel-to-pixel uniformity should be urgently improved.…”

Section: Introductionmentioning

confidence: 99%

Rheological engineering of perovskite suspension toward high-resolution X-ray flat-panel detector

Song

et al. 2023

Preprint

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Solution-processed polycrystalline perovskite film is promising for the next generation X-ray imaging. However, the spatial resolution of current perovskite X-ray panel detectors is far lower than the theoretical limit. Herein we find that the pixel level non-uniformity, also known as fixed pattern noise (FPN), is the chief culprit affecting the signal-to-noise ratio and reducing the resolution of perovskite detectors. We report a synergistic strategy of rheological engineering the perovskite suspensions to achieve X-ray FPDs with pixel-level high uniformity and near-to-limit spatial resolution. Our approach includes the addition of methylammonium iodide and polyacrylonitrile to the perovskite suspension, to synergistically enhance the flowability and particle stability of the oversaturated solution. The obtained suspension perfectly suits for the blade-coating process, avoiding the uneven distribution of solutes and particles within perovskite films. The assembled perovskite panel detector exhibits greatly improved FPN value (1.39%), high sensitivity (6.3×105 μC Gyair-1 cm−2), low detection limit (14.27 nGyair·s-1) as well as good working stability, close to the performance of single crystal detectors. Most importantly, the detector achieves a resolution of 0.51 lp/pix, exceeding all previous perovskite X-ray flat-panel detectors.

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A Median-Ratio Scene-Based Non-Uniformity Correction Method for Airborne Infrared Point Target Detection System

Cited by 5 publications

References 24 publications

Research on shutter-less non-uniformity correction technology based on ambient temperature

Research on shutter-less non-uniformity correction technology based on ambient temperature

Case Study on the Fitting Method of Typical Objects

Rheological engineering of perovskite suspension toward high-resolution X-ray flat-panel detector

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