Modeling And Simulation Of Infrared Imaging Sensors And Fidelity Evaluation Of Camera Simulators

Since prototyping a real infrared camera is costly and time-consuming, there is a growing demand for infrared image sensor designers and imaging system analysts to optimize design tradeoffs and predict image quality via computer simulation before an actual camera becomes available. Hence, during the past several decades, a great variety of infrared virtual camera simulators have been developed to achieve such a goal. These simulators show different performance for different applications. The main content of this dissertation is summarized as follows.Aiming at the new generation of infrared staring imaging sensors, the primary physical effects of imaging system elements are modeling based on thermal infrared radiative transfer and energy conversion in imaging physical processes. The simulation model, which is different from those qualitative models available now, quantitatively represents the modulation characteristics of input information by design parameters of imaging system elements. Through building the signal response model, spatial transfer model, spatial sampling model, and noise characterization model for infrared imaging sensors, we can establish a perfect simulator and realize high-fidelity simulation for the imaging process. For credibility validation of the simulation model, an experiment platform is constructed to capture the system performance curve and output image of a real infrared camera. Subsequently the similarities of performance curves and output images between the simulator and the real device are calculated and compared, and a quantitive evaluation of the simulation fidelity is made. Finally, the credibility of the simulation model is validated by the fidelity data.Aiming to improve the current situation of the simulator fidelity evaluation system, a new theoretical framework based on fuzzy logic and fuzzy set theory is introduced to quantitatively evaluate the simulation fidelity of infrared camera simulators. This framework is an objective and multi-parameter evaluation system, which is related to many fields such as fuzzy logic, curve similarity evaluation, image quality assessment, and information theory, etc. Compared with those available, a multi-scale fidelity evaluation analysis, which includes performance characteristic evaluation of the total system and image quality assessment of the camera outputs, is considered more compromisingly in this evaluation system. Consequently, the new framework here has better task flexibility and it is easier to quantify and compare the fidelity results between different camera simulators with the same or similar mission requirements.In this work, we have investigated the current status of infrared simulator fidelity evaluation systems and proposed a fuzzy-logic-based, multi-scale, quantitative theoretical fidelity evaluation framework. This framework is different from any other evaluation system, which makes overall evaluation and takes both the system performance curves and the output image qualities into consideration. Due to including multiple adjustable weight coefficients and in the evaluation framework, it can determine the optimal weight coefficient set for a specific simulation mission and theoretically it will have better mission flexibility than other methods. In a specific simulation mission situation, it is easy to make a comparison between the fidelity evaluation results from different camera simulators.