Shutter-less calibration of uncooled infrared cameras

Abstract

Abstract. Infrared (IR) cameras based on microbolometer focal plane arrays (FPAs) are the most widely used cameras in thermography. New fields of applications like handheld devices and small distributed sensors benefit from the latest sensor improvements in terms of cost and size reduction. In order to compensate for disturbing influences derived from changing ambient conditions, radiometric cameras use an optical shutter for online recalibration purposes, partially also together with sensor temperature stabilization. For these new applications, IR cameras should consist only of infrared optics, a sensor array, and digital signal processing (DSP). For acceptable measurement uncertainty values without using an optical shutter (shutter-less), the disturbing influences of changing thermal conditions have to be treated based on temperature measurements of the camera interior. We propose a compensation approach based on calibration measurements under controlled ambient conditions. All correction parameters are determined during the calibration process. Without sensor temperature stabilization (TEC-less), the pixel responsivity is also affected by the camera temperature changes and has to be considered separately. This paper presents the details of the compensation procedure and discusses relevant aspects to gain low temperature measurement uncertainty. The residual measurement uncertainty values are compared to the shutter-based compensation approach.

An advanced radiometric calibration approach for uncooled thermal cameras

“…. Therefore, the inverse function equation serves to convert image values into object temperature values (Tempelhahn et al., ):where = final object‐related grey value at pixel ; T O ( r , c ) = calculated object temperature at pixel ; and R , B , F , O = Planck curve parameters; F is usually set to 1·0.…”

“…An alternative method is to combine FPA temperature with the updating of correction parameters. Several temporal NUC-correction methods based on signal processing have been presented, for example, by polynomial curve fitting (Cao and Tisse, 2013), piecewise Lagrange interpolation (Liang et al, 2017) and multivariate regression modelling (Tempelhahn et al, 2016). The main benefit of these techniques is that continuous measurement is enabled.…”

“…Every commercial thermal camera has its own response curve when the FPA temperature is stable, such as a linear model (Nugent et al, 2013) or a second-order model (Cao and Tisse, 2013;Budzier and Gerlach, 2015;Tempelhahn et al, 2016). However, this response curve can only be obtained by laboratory experiments because of non-disclosed manufacturer information.…”

“…7. Therefore, the inverse function equation (5) serves to convert image values into object temperature values (Tempelhahn et al, 2016):…”

Temperature drift compensation in metrological microbolometer camera using multi sensor approach

“…There is an approach which involves the use of several temperature sensors located in different places inside the camera [10]. This solution enables the correction of the signal in the thermal transient state of the camera.…”