Due to the large quantity of low-cost, high-speed computational processing available today, computational imaging (CI) systems are expected to have a major role for next generation multifunctional cameras. The purpose of this work is to quantify the performance of theses CI systems in a standardized manner. Due to the diversity of CI system designs that are available today or proposed in the near future, significant challenges in modeling and calculating a standardized detection signal-to-noise ratio (SNR) to measure the performance of these systems. In this paper, we developed a path forward for a standardized detectivity metric for CI systems. The detectivity metric is designed to evaluate the performance of a CI system searching for a specific known target or signal of interest, and is defined as the optimal linear matched filter SNR, similar to the Hotelling SNR, calculated in computational space with special considerations for standardization. Therefore, the detectivity metric is designed to be flexible, in order to handle various types of CI systems and specific targets, while keeping the complexity and assumptions of the systems to a minimum.
Bradley L. Preece and George Nehmetallah, "A computational imaging target specific detectivity metric," Proc. SPIE 10178, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVIII, 101780M (Presented at SPIE Defense + Security: April 12, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2263585.
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