This paper describes a camera simulation framework for validating machine vision algorithms under general airborne camera imperfections. Lens distortion, image delay, rolling shutter, motion blur, interlacing, vignetting, image noise, and light level are modelled. This is the first simulation that considers all temporal distortions jointly, along with static lens distortions in an online manner. Several innovations are proposed including a motion tracking system allowing the camera to follow the flight log with eligible derivatives. A reverse pipeline, relating each pixel in the output image to pixels in the ideal input image, is developed. It is shown that the inverse lens distortion model and the inverse temporal distortion models are decoupled in this way. A short-time pixel displacement model is proposed to solve for temporal distortions (i.e. delay, rolling shutter, motion blur, and interlacing). Evaluation is done by several means including regenerating an airborne dataset, regenerating the camera path on a calibration pattern, and evaluating the ability of the time displacement model to predict other frames. Qualitative evaluations are also made.

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