Proc. SPIE. 5784, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVI
KEYWORDS: Long wavelength infrared, Signal to noise ratio, Optical design, Imaging systems, Sensors, Interference (communication), Wavefronts, Signal processing, Modulation transfer functions, Systems modeling
In a long wave infrared (LWIR) system there is the need to capture the maximum amount of information of objects over a broad volume for the identification and classification by the human or machine observer. In a traditional imaging system the optics limit the capture of this information to a narrow object volume. This limitation can hinder the observer's ability to navigate and/or identify friend or foe in combat or civilian operations. By giving the observer a larger volume of clear imagery their ability to perform will drastically improve. The system presented allows the efficient capture of object information over a broad volume and is enabled by a technology called Wavefront Coding. A Wavefront Coded system employs the joint optimization of the optics, detection and signal processing. Through a specialized design of the system’s optical phase, the system becomes invariant to the aberrations that traditionally limit the effective volume of clear imagery. In the process of becoming invariant, the specialized phase creates a uniform blur across the detected image. Signal processing is applied to remove the blur, resulting in a high quality image. A device specific noise model is presented that was developed for the optimization and accurate simulation of the system. Additionally, still images taken from a video feed from the as-built system are shown, allowing the side by side comparison of a Wavefront Coded and traditional imaging system.
Understanding signal and noise quantities in any practical computational imaging system is critical. Knowledge of the imaging environment, optical parameters, and detector sensitivity determine the signal quantities but often noise quantities are assumed to be independent of the signal and either uniform or Gaussian additive. These simplistic noise models do not accurately model actual detectors. Accurate noise models are needed in order to design optimal systems. We describe a noise model for a modern APS CMOS detector and a number of noise sources that we will be measuring. A method for characterizing the noise sources given a set of dark images and a set of flat field images is outlined. The noise characterization data is then used to simulate dark images and flat field images. The simulated data is a very good match to the real data thus validating the model and characterization procedure.