  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.
