论文标题
使用高斯的混合物在不均匀网格上的插值点
Interpolating Points on a Non-Uniform Grid using a Mixture of Gaussians
论文作者
论文摘要
在这项工作中，我们提出了一种基于高斯混合模型进行非均匀图像插值的方法。传统的图像插值方法，例如最近的邻居，双线性，锤子，兰斯佐斯（Lanczos）等。假设您要从中插入的坐标位于均匀的网格上。但是，在实践中并不总是如此，我们开发了一种能够从任意定位的像素值生成图像的插值方法。我们通过将每个已知像素表示为2D正态分布，并将每个已知图像像素作为所有已知的混合物的样品来做到这一点。除了能够从任意定位的像素集重建图像的能力外，这还使我们可以通过插值过程进行区分，这可能有助于下游应用程序。我们优化的CUDA内核和重现基准测试的源代码位于https://github.com/universome/non-uniform-inster-interpolation。
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.
