Thermal imaging and sensing market include devices relying on microbolometer arrays, thermopiles, or pyroelectric sensors. Between 2019 and 2022, demand peaked pulled by fever detection systems used to fight Covid-19 pandemic and remained significant in years after despite lower sales in 2022 due to global chip shortage situation. After ramping-up their production in 2020, Chinese players eventually doubled there share of thermal imager global shipments and managed to stabilize it by adapting their offer. Next wave of demand for thermal imagers might come from automotive for AEB (Autonomous Emergency Braking), with the potential help of safety regulations. Meanwhile thermopiles have been recently integrated in a new model of consumer smartwatch.
Unlike photographic image sensors with infrared cutoff filter, low light image sensors gather light over visible and near infrared (VIS-NIR) spectrum to improve sensitivity. However, removing infrared cutoff filter makes the color rendering challenging. In addition, no color chart, with calibrated infrared content, is available to compute color correction matrix (CCM) of such sensors. In this paper we propose a method to build a synthetic color chart (SCC) to overcome this limitation. The choice of chart patches is based on a smart selection of spectra from open access and our own VIS-NIR hyperspectral images databases. For that purpose we introduce a fourth cir dimension to CIE-L*a*b* space to quantify the infrared content of each spectrum. Then we uniformly sample this L*a*b*cir space, leading to 1498 spectra constituting our synthetic color chart. This new chart is used to derive a 3x4 color correction matrix associated to the commercial RGB-White sensor (Teledyne-E2V EV76C664) using a classical linear least square minimization.. We show an improvement of signal to noise ratio (SNR) and color accuracy at low light level compared to standard CCM derived using Macbeth color chart.
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