1,057 research outputs found

    Fixed pattern noise compensation in a mercury cadmium telluride infrared focal plane array

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    Bibliography: pages 106-109.This thesis describes techniques for the correction of spatial noise artifacts in a mercury cadmium telluride infrared camera system. The spatial noise artifacts are a result of nonuniformities within the infrared focal plane detector array. The techniques presented dispense with the need for traditional temperature references, and provide nonuniformity compensation by using only the statistics of the moving infrared scene and motion of the camera assembly for calibration. Frame averaging is employed, assuming that all of the detector pixels will eventually be irradiated with the same levels of incident flux after some extended period of time. Using a statistical analysis of the camera image data, the correction coefficients are re-calculated and updated. These techniques also ensure that the calculated coefficients continually track the variations in the dark currents as well as temperature changes within the dewar sensor cooling vessel. These scene-based reference free approaches to the calculation of compensation coefficients in the infrared camera are shown to be successful in compensating for the effects of fixed pattern spatial noise

    Improving temperature estimation in low-cost infrared cameras using deep neural networks

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    Low-cost thermal cameras are inaccurate (usually Ā±3āˆ˜C\pm 3^\circ C) and have space-variant nonuniformity across their detector. Both inaccuracy and nonuniformity are dependent on the ambient temperature of the camera. The main goal of this work was to improve the temperature accuracy of low-cost cameras and rectify the nonuniformity. A nonuniformity simulator that accounts for the ambient temperature was developed. An end-to-end neural network that incorporates the ambient temperature at image acquisition was introduced. The neural network was trained with the simulated nonuniformity data to estimate the object's temperature and correct the nonuniformity, using only a single image and the ambient temperature measured by the camera itself. Results show that the proposed method lowered the mean temperature error by approximately 1āˆ˜C1^\circ C compared to previous works. In addition, applying a physical constraint on the network lowered the error by an additional 4%4\%. The mean temperature error over an extensive validation dataset was 0.37āˆ˜C0.37^\circ C. The method was verified on real data in the field and produced equivalent results

    Simultaneous temperature estimation and nonuniformity correction from multiple frames

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    Infrared (IR) cameras are widely used for temperature measurements in various applications, including agriculture, medicine, and security. Low-cost IR camera have an immense potential to replace expansive radiometric cameras in these applications, however low-cost microbolometer-based IR cameras are prone to spatially-variant nonuniformity and to drift in temperature measurements, which limits their usability in practical scenarios. To address these limitations, we propose a novel approach for simultaneous temperature estimation and nonuniformity correction from multiple frames captured by low-cost microbolometer-based IR cameras. We leverage the physical image acquisition model of the camera and incorporate it into a deep learning architecture called kernel estimation networks (KPN), which enables us to combine multiple frames despite imperfect registration between them. We also propose a novel offset block that incorporates the ambient temperature into the model and enables us to estimate the offset of the camera, which is a key factor in temperature estimation. Our findings demonstrate that the number of frames has a significant impact on the accuracy of temperature estimation and nonuniformity correction. Moreover, our approach achieves a significant improvement in performance compared to vanilla KPN, thanks to the offset block. The method was tested on real data collected by a low-cost IR camera mounted on a UAV, showing only a small average error of 0.27āˆ˜Cāˆ’0.54āˆ˜C0.27^\circ C-0.54^\circ C relative to costly scientific-grade radiometric cameras. Our method provides an accurate and efficient solution for simultaneous temperature estimation and nonuniformity correction, which has important implications for a wide range of practical applications

    Performance assessment of low-cost thermal cameras for medical applications

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    Thermal imaging is a promising technology in the medical field. Recent developments in low-cost infrared (IR) sensors, compatible with smartphones, provide competitive advantages for home-monitoring applications. However, these sensors present reduced capabilities compared to more expensive high-end devices. In this work, the characterization of thermal cameras is described and carried out. This characterization includes non-uniformity (NU) effects and correction as well as the thermal camerasĀ“ dependence on room temperature, noise-equivalent temperature difference (NETD), and response curve stability with temperature. Results show that low-cost thermal cameras offer good performance, especially when used in temperature-controlled environments, providing evidence of the suitability of such sensors for medical applications, particularly in the assessment of diabetic foot ulcers on which we focused this study.This research was funded by the IACTEC Technological Training program, grant number TF INNOVA 2016-2021, and by the European Union Interreg-Mac funding program, grant number MAC/1.1.b/098 (MACbioIDi project)

    Polarimetric Calibration and Characterization of the Telops Field Portable Polarimetric-Hyperspectral Imager

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    The Telops polarimetric-hyperspectral imager combines polarimetric and hyperspectral technologies to enable enhanced scene characterization. The Defense Threat Reduction Agency funded research at AFIT to leverage this capability to provide more accurate scene information to radiation transport models that will allow for more effective location of radiation sources within a region of interest. To support the objectives of the DTRA effort, there is a requirement for highly accurate radiometric, polarimetric, and spectral data on a pixel-by-pixel basis. The complex nature of the Telops instrument combined with working in the thermal IR waveband makes achieving this accuracy a challenge. This thesis develops a calibration methodology that enables high data accuracy in all three domains. In the process, a mathematical calibration framework was developed that links standard Fourier transform spectrometer (FTS) calibration with standard polarimetric calibration in a straightforward manner. This provided a framework for understanding the influence of various instrument parameters (both ideal and non-ideal) on ultimate calibration performance. The framework developed is utilized to quantify the non-idealities of the system and to characterize the performance of the spectro-polarimetric calibration. Additionally, fundamental performance limits are characterized including the noise equivalent spectral radiance and noise equivalent degree of linear polarization of the system

    Uncertainty estimation of temperature coefficient measurements of PV modules

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    Temperature coefficients of PV modules play an important role in distinguishing between products in an increasingly competitive market. However, measurement setups vary greatly and inter-laboratory comparisons show deviations from the mean of around Ā±10-15 %, or even larger, for temperature coefficients of maximum power. Measurement deviations often do not agree with the uncertainty estimates indicating that uncertainty is significantly underestimated. On the other hand, some laboratories have adopted a very conservative approach and needlessly overestimate the uncertainty. A new and robust methodology for calculating the temperature coefficients is presented here. This includes estimating and propagating the uncertainty of different types of measurement systems and procedures, in accordance with international standards. The method is validated with a round-robin inter-comparison. Two c Si modules were measured with five different measurement setups with uncertainties estimated following the proposed approach. The advanced uncertainty estimation method resulted in a decrease of the estimated uncertainty of all systems by a minimum of 50 % compared to the previous conservative estimates, enabling us to identify a previously unknown systematic effect. The measurement results of one of the systems were inconsistent with the estimated uncertainty. Further investigation confirmed a systematic effect due to the poor spectrum of that system. Removing the outlier measurement, the measurement percentage deviation from the reference value for maximum power temperature coefficients was within Ā±3.2%. The deviation was consistent with the stated uncertainties. The approach can facilitate the reduction of temperature coefficient measurements uncertainty by highlighting areas of improvement for bespoke systems

    Design and Model Verification of an Infrared Chromotomographic Imaging System

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    A prism chromotomographic hyperspectral imaging sensor is being developed to aid in the study of bomb phenomenology. Reliable chromotomographic reconstruction depends on accurate knowledge of the sensor specific point spread function over all wavelengths of interest. The purpose of this research is to generate the required point spread functions using wave optics techniques and a phase screen model of system aberrations. Phase screens are generated using the Richardson-Lucy algorithm for extracting point spread functions and Gerchberg-Saxton algorithm for phase retrieval. These phase screens are verified by comparing the modeled results of a blackbody source with measurements made using a chromotomographic sensor. The sensor itself is constructed as part of this research. Comparison between the measured and simulated results is based upon the noise statistics of the measured image. Four comparisons between measured and modeled data, each made at a different prism rotation angle, provide the basis for the conclusions of this research. Based on these results, the phase screen technique appears to be valid so long as constraints are placed on the field of view and spectral region over which the screens are applied

    Dynamic infrared scene simulation using grayscale modulation of digital micro-mirror device

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    AbstractDynamic infrared scene simulation is for discovering and solving the problems encountered in designing, developing and manufacturing infrared imaging guidance weapons. The infrared scene simulation is explored by using the digital grayscale modulation method. The infrared image modulation model of a digital micro-mirror device (DMD) is established and then the infrared scene simulator prototype which is based on DMD grayscale modulation is developed. To evaluate its main parameters such as resolution, contrast, minimum temperature difference, gray scale, various DMD subsystems such as signal decoding, image normalization, synchronization drive, pulse width modulation (PWM) and DMD chips are designed. The infrared scene simulator is tested on a certain infrared missile seeker. The test results show preliminarily that the infrared scene simulator has high gray scale, small geometrical distortion and highly resolvable imaging resolution and contrast and yields high-fidelity images, thus being able to meet the requirements for the infrared scene simulation inside a laboratory

    Proceedings of the Second Airborne Imaging Spectrometer Data Analysis Workshop

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    Topics addressed include: calibration, the atmosphere, data problems and techniques, geological research, and botanical and geobotanical research
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