Inverse distortion is used to create an undistorted image from a distorted image. For each pixel in the undistorted image it is required to determine which pixel in the distorted image should be used. However the process of characterizing a lens using a model such as that of Brown, yields a non-invertible mapping from the distorted domain to the undistorted domain. There are three current approaches to solving this: an approximation of the inverse distortion is derived from a low-order version of Brown's model; an initial guess for the distorted position is iteratively refined until it yields the desired undistorted pixel position; or a look-up table is generated to store the mapping. Each approach requires one to sacrifice either accuracy, memory usage or processing time. This paper shows that it is possible to have real-time, low memory, accurate inverse distortion correction. A novel method based on the re-use of left-over distortion characterization data is combined with modern numerical optimization techniques to fit a high-order version of Brown's model to characterize the inverse distortion. Experimental results show that, for thirty-two 5mm lenses exhibiting extreme barrel distortion, inverse distortion can be improved 25 fold to 0.013 pixels RMS over the image.

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Multiple recirculations through an optical buffer using a fast-reconfigurable AVC based Crosspoint switch matrix is shown. A 10Gbit/s payload is used and a small power penalty for each additional recirculation is achieved.

Most current lens distortion models use only a few terms of Brown's model, which assumes that the radial distortion is dependant only on the distance from the distortion centre, and an additive tangential distortion can be used to correct lens de-centering. This paper shows that the characterization of lens distortion can be improved by over 79% compared to prevailing methods. This is achieved by using modern numerical optimization techniques such as the Leapfrog algorithm, and sensitivity-normalized parameter scaling to reliably and repeatably determine more terms for Brown's model. An additional novel feature introduced in this paper is to allow the distortion to vary not only with polar distance but with the angle too. Two models for radially asymmetrical distortion (i.e. distortion that is dependant on both polar angle and distance) are discussed, implemented and contrasted to results obtained when no asymmetry is modelled. A sample of 32 cameras exhibiting extreme barrel distortion (due to their 6.0mm focal lengths) is used to show that these new techniques can straighten lines to within 7 hundredths of a pixel RMS over the entire image.

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