Cooled infrared detectors are typically characterized by well-known electro-optical parameters: responsivity, noise equivalent temperature difference, shot noise, 1/f noise, and so on. Particularly important for staring arrays is also the residual fixed pattern noise (FPN) that can be obtained after the application of the nonuniformity correction (NUC) algorithm. A direct measure of this parameter is usually hard to define because the residual FPN strongly depends, other than on the detector, on the choice of the NUC algorithm and the operative scenario. We introduce three measurable parameters: instability, nonlinearity, and a residual after a polynomial fitting of the detector response curve, and we demonstrate how they are related to the residual FPN after the application of an NUC (the relationship with three common correction algorithms is discussed). A comparison with experimental data is also presented and discussed.
Due to the fast-growing of cooled detector sensitivity in the last years, on the image 10-20 mK temperature difference between adjacent objects can theoretically be discerned if the calibration algorithm (NUC) is capable to take into account and compensate every spatial noise source. To predict how the NUC algorithm is strong in all working condition, the modeling of the flux impinging on the detector becomes a challenge to control and improve the quality of a properly calibrated image in all scene/ambient conditions including every source of spurious signal. In literature there are just available papers dealing with NU caused by pixel-to-pixel differences of detector parameters and by the difference between the reflection of the detector cold part and the housing at the operative temperature. These models don’t explain the effects on the NUC results due to vignetting, dynamic sources out and inside the FOV, reflected contributions from hot spots inside the housing (for example thermal reference far of the optical path). We propose a mathematical model in which: 1) detector and system (opto-mechanical configuration and scene) are considered separated and represented by two independent transfer functions 2) on every pixel of the array the amount of photonic signal coming from different spurious sources are considered to evaluate the effect on residual spatial noise due to dynamic operative conditions. This article also contains simulation results showing how this model can be used to predict the amount of spatial noise.
The raw output of a generic infrared vision system, based on staring arrays, is spatially not uniform. This spatial noise
can be much greater than the system NETD, and determines a strong drop in system performance.
Therefore we need to model all system non-uniformity (NU) sources to highlight the parameters that should be
controlled by optical and mechanical design, the ones depending on the focal plane array and those that can be corrected
In this paper, we identify the main NU sources (optical relative irradiance, housing straylight, detector pixel-pixel
differences and non linearity), we show how to model these sources and how they are related to the design and physical
parameters of the system. We then describe the total signal due to these sources at the detector output. Applying different
NUC algorithms to this signal, the final results on the image can be simulated finding a proper correction algorithm. At
the end we show the agreement between the model with the experimental data taken on a real system.
Changing a limited set of parameters, this model can be applied to many third generation thermal imager configurations.