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Optimization of Photometric Warp SFSR Noise Transfer in Thermal Imaging Upscalers

In this work, we consider noise optimization of thermal-imaging upscalers incorporating Photometric Warp (p-Warp) Single-Frame Superresolution (SFSR). This topic is of concern where designers might employ uncooled microbolometer FPA sensors due to the fact nonlinear reconstruction filtering on the characteristically high noise floor of these devices gives rise to secondary noise upconversion processes. At sufficiently high amplitude, this surplus noise will then degrade image reconstruction performance.Noise upconversion pathways are traced to nonlinear mixing associated with warp reconstruction filter and pattern manifold assembly operations for which mathematical models are developed. In each case, an additive, level-dependent noise is indicated, manifesting as amplitude and edge-dislocation errors respectively. Monte Carlo simulation confirms edge-dislocation as both amplitude-dependent and predominant in terms of noise power transfer, subsequent removal of which exposes p-Warp filter noise transfer as approximately Gaussian and with a much reduced amplitude dependence. Thus, with sufficient attenuation of pattern manifold noise, system noise power transfer is effectively linearized, enabling derivation of an optimal noise-shaping via standard LTI matched-filter design theory. Accordingly, with casting of the upscaler noise optimization problem as one of maximizing edge-contour reconstruction SNR, an optimal low-pass noise-shaping filter characteristic is calculated and confirmed based upon a realized PSNR gain. Content is organized as follows; (1) Introduction, (2) Pattern Manifold Assembly (PMA) Noise Transfer, (3) Photometric Warp Engine (PWE) Noise Transfer (4) Monte Carlo Simulation, (5) Discussion, (6) Summary.