KEYWORDS: Sensors, 3D metrology, Thermography, Nonuniformity corrections, Analog electronics, Cameras, Image sensors, 3D modeling, 3D acquisition, Infrared sensors
Uncooled staring thermal imagers have noise characteristics that are different from cooled thermal imagers (photon detector sensors). For uncooled sensors, typical measurements of some noise components can vary as much as 3 to 5 times the original noise value. Additionally, the detector response often drifts to the point that non-uniformity correction is only good for a short time period. Because the noise can vary so dramatically with time, it can prove difficult to measure the noise associated with uncooled systems. However, it is critical that laboratory measurements provide repeatable and reliable measurement of constructed uncooled thermal imagers. In light of the above difficulties, a primary objective of this research has been to develop a satisfactory measurement for the noise of uncooled staring thermal imagers. In this research effort, three-dimensional noise (3D Noise) data vs. time was collected for several uncooled sensors after nonuniformity correction. Digital and analog noise data vs. time were collected nearly simultaneously. Also, multiple 3D Noise vs. time runs were made to allow the examination of variability. Measurement techniques are being developed to provide meaningful and repeatable test procedures to characterize the uncooled systems.
The two most important characteristics of every infrared imaging system are its resolution and its sensitivity. The resolution is limited by the system's Modulation Transfer Function (MTF), which is typically measurable. System sensitivity is limited by noise, which for infrared systems is usually thought of as a Noise Equivalent Temperature Difference (NETD). However, complete characterization of system noise in modern systems requires the 3D-Noise methodology (developed at NVESD), which separates the system noise into 7 orthogonal components including both temporal-varying and fixed-pattern noises. This separation of noise components is particularly relevant and important in characterizing Focal Plane Arrays (FPA), where fixed-pattern noise can dominate. Since fixed-pattern noise cannot be integrated out by post-processing or by the eye, it is more damaging to range performance than temporally-varying noise. While the 3D-Noise methodology is straightforward, there are several important practical considerations that must be accounted for in accurately measuring 3D Noise in the laboratory. This paper describes these practical considerations, the measurement procedures used in the Advanced Sensor Evaluation Facility (ASEF) at NVESD, and their application to characterizing modern and future infrared imaging systems.
The Minimum Resolvable Temperature Difference (MRTD or MRT) is the most widely accepted and inclusive figure of merit for describing a thermal imaging system's performance. It is the product of analytic mathematical models and traditional man-in-loop system hardware performance measurements that describe IR systems. MRT is a basis for thermal field performance model predictions and is commonly used in specification of thermal imagers. The MRT test is subjective because it requires human observers to just discern increasingly smaller 4-bar patterns as a function of temperature differences between bars and the background. When performed by trained observers, the MRT test is an accurate measure of sensitivity as a function of spatial resolution. The ability to resolve 4-bar patterns varies between observers. Furthermore, MRT is a psychophysical task, for which biases are unavoidable. In this paper, uncertainties in MRT measurements are reported for individual trained observers and between observers as functions of some biases, such as random and fixed pattern noise. For this paper, virtual MRTs were performed on a new, custom visual acuity test simulator, developed for NVESD, that allows precise control over significant sensor and display parameters, and these results are compared. Through a process of eliminating sources of MRT variability, we have been able to quantify the observer variability.
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