Imaging system design and performance depend upon a myriad of radiometric, spectral, and spatial parameters. The “bare bones” sensor consists of optics, detector, display, and an observer. Range degrading parameters include 3D noise, optical blur, and pixel interpolation. Scenario parameters include detection, recognition, and identification probability, target contrast, target size, line-of-sight motion, and atmospheric conditions. Generally, the customer provides the scenario and the analyst optimizes sensor parameters to achieve maximum acquisition range. A wide variety of programs have been available in the past (e.g., SSCamIP, NVThermIP etc.). These programs have been consolidated into the Night Vision Integrated Performance Model (NVIPM). For convenience, the calculations are performed in the frequency domain (MTF analysis). This is often called image chain modeling. Although the math is sometimes complex, the equations are graphed for easy interpretation. NVIPM can easily perform trade studies and provides a gradient (sensitivity) analysis. Gradient analysis lists those parameters (in decreasing order) that affect acquisition range. This course consists of 6 sections: (1) The history of imaging system design and the transition from scanning arrays to staring arrays, (2) imaging system chain analysis covering MTF theory, “bare bones” system design, environmental effects (atmospheric attenuation, turbulence, and line-of-sight motion a.k.a. jitter), sampling artifacts, and image processing, (3) detector responsivity, radiometry, various noise sources (photon, dark current, read) and the resulting SNR, (4) targets, backgrounds, and target signatures, (5) various image quality metrics which includes NVIPM, and (6) acquisition range and trade studies. By far, the most important section is the trade study graphical representations. Three optimization examples are provided (case study examples): long range imaging, short range imaging, and IRST systems. While the course emphasizes infrared system design, it applies to visible, NIR, and short infrared (SWIR) systems. From an optimization viewpoint, the only difference across the spectral bands is the target signature nomenclature. When considering hardware design, the spectral region limits lens material and detector choices.

As imaging technologies rapidly evolve, there's an increasing demand for professionals who can work across multiple platforms, understand the underlying assumptions of various tests, and ensure accurate measurements for inter-laboratory comparisons. By providing a thorough grounding in quantitative and qualitative metrics applicable to a wide range of imaging systems, this course promotes standardization and consistency in testing procedures. It bridges the gap between theory and practice, enabling professionals to adapt to technological advancements and make informed decisions about test applicability and limitations. The test concepts presented apply to CCD/CMOS cameras, intensified CCD cameras, night vision goggles, SWIR cameras, and infrared cameras. Using a linear systems approach, this course describes all the quantitative and qualitative metrics that are commonly used to characterize imaging system performance. Laboratory performance parameters discussed include spatial sampling (distortion), modulation transfer function (MTF), 3D noise, photon transfer curve (PTC), minimum resolvable temperature (MRT), and the minimum resolvable contrast (MRC), temporal response, quantum efficiency, and system of system level tests (latency etc.). Concepts related to data validation and setup verification are described along with many common best practices. Data reduction and analysis techniques are reviewed outlining many of the corresponding assumptions that must be met for accurate measurements. This course will also provide demonstrations of some of the common laboratory test techniques on commercially available thermal and color cameras.

The imaging system analyst must be conversant in numerous diverse technologies. Each has a unique effect on system evaluation. This course highlights these technologies in 10 sections and is filled with numerous practical and useful examples. While the equations are provided, the concepts are presented graphically and with imagery. An engineering approach is taken: the "bare bones" imaging system consists of illumination, optics, detector, and display. The radiometry and photometry section compares calibration sources to real sources (sun, fading twilight, artificial sources). The optics/detector combination performance can be described in the frequency domain (MTF analysis) by the parameter Fλ/d which is the ratio of the detector cutoff to the optics cutoff. Equally important, but often neglected is sampling; an inherent feature of all electronic imaging systems. Sampling artifacts, which creates blocky images, are particularly bothersome with periodic targets such as test targets and bar codes. Sampling cannot be studied in isolation but requires a reconstruction filter. Sampling artifacts are illustrated through numerous imagery. For man-in-the-loop operation, the display and the eye are of concern and, in many situations, these limit the over system performance. The impact of viewing distance on the image quality of displays, TVs, computers, cell phones, and halftones is discussed. A point-and-shoot camera appears simplistic from the outside. However, as shown in an example, modeling can be quite complex. <br/> The math, statistics, and data analysis section covers the validity of approximations, central limit theorem, Gaussian statistics, decision theory, and the receiver operating curve (ROC). Included are different ways to graph data and, as an example, the margin of error reported in political polls. System resolution (a.k.a. image quality) can be inferred from Schade's equivalent resolution which is a function of Fλ/d. Atmospheric transmittance and glare (via the sky-to-ground ratio) is discussed.<br/> Target acquisition is presented with a simplified, back-of-the-envelope, approach using Fλ/d. Early systems had "large" detectors (Fλ/d &lt; 0.5). These systems were detector limited and acquisition range was inversely proportional to detector size. With "small" detectors (Fλ/d &lt; 1.5) the system is optics limited. Here changing the detector size has minimal effect on range. Selecting a mid-wave (MWIR) or long-wave (LWIR) infrared sensor depends upon Fλ/d, sensor noise, and atmospheric conditions.

This course describes the quantitative and qualitative metrics that are used to characterize CCD arrays and solid-state cameras. Certain specifications are important for specific applications. These include sensitivity (responsivity), resolution, signal-to-noise ratio, dynamic range, and the modulation transfer function. Responsivity depends critically upon the source spectral output and the camera spectral response. The effects of shot, reset, fixed pattern, and readout noise are described.

Sampling is an inherent feature of all electronic imaging systems. Sampling creates aliasing, MoirÚ patterns, and variations in object edge location and width. The most obvious effect is a "blocky" or pixelated image. Sampling affects data interpretation and displayed imagery. These effects are rarely reported when viewing natural scenery although present. They can dominate machine vision system performance, camera characterization, and resolution. The optical blur diameter, detector size, and f-number are combined into a system-level resolution metric. This composite is a better metric than just the blur diameter or detector angular subtense. To satisfy the sampling theorem, the signal must be band-limited, the digitizer must sample the signal at an adequate rate, and a low-pass reconstruction filter must be present. These conditions are not present in today's camera systems and the reconstructed image cannot appear exactly as the original. While image processing algorithms remove some defects, they always introduce additional (unwanted) artifacts. Acceptance of these artifacts depends upon the specific application. Imagery illustrating zoom, smoothing, and other algorithms are presented.

Thermal imaging system applications range from construction applications to electrical and mechanical inspections. It includes search and rescue, endangered species monitoring, border patrol, law enforcement, military applications and surveillance which includes people and objects. This course explains how nondestructive testing can locate flaws on commercial aircraft. A thermal imaging system that evaluates the condition of power lines, transformers, circuit breakers, motors, printed circuit boards, and other electronic components is described. Heat transfer, radiation theory, and emissivity are the parameters define the target signature. The environment (sun, wind, or other hot targets) may further modify the target signature. This course presents these characteristics and applications.