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    Interpolating Points on a Non-Uniform Grid using a Mixture of Gaussians

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    Type
    Preprint
    Authors
    Skorokhodov, Ivan
    KAUST Department
    KAUST.
    Date
    2020-12-24
    Permanent link to this record
    http://hdl.handle.net/10754/666750
    
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    Abstract
    In this work, we propose an approach to perform non-uniform image interpolation based on a Gaussian Mixture Model. Traditional image interpolation methods, like nearest neighbor, bilinear, Hamming, Lanczos, etc. assume that the coordinates you want to interpolate from, are positioned on a uniform grid. However, it is not always the case in practice and we develop an interpolation method that is able to generate an image from arbitrarily positioned pixel values. We do this by representing each known pixel as a 2D normal distribution and considering each output image pixel as a sample from the mixture of all the known ones. Apart from the ability to reconstruct an image from arbitrarily positioned set of pixels, this also allows us to differentiate through the interpolation procedure, which might be helpful for downstream applications. Our optimized CUDA kernel and the source code to reproduce the benchmarks is located at https://github.com/universome/non-uniform-interpolation.
    Publisher
    arXiv
    arXiv
    2012.13257
    Additional Links
    https://arxiv.org/pdf/2012.13257
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