Stochastic bilateral filter for high-dimensional images

We propose a stochastic bilateral filter (SBF) - fast image filtering aimed at processing high dimensional images (such as color and hy-perspectral images). SBF is comprised of an efficient randomized process, where it agrees with conventional bilateral filter (BF) on average. By Monte-Carlo, we repeat this process a few times with different random instantiations so that they can be averaged to attain the correct BF output. The computational bottleneck of the SBF is constant with respect to the color dimension, meaning the complexity for filtering hyperspectral images is nearly the same as the grayscale images. It is considerably faster than the conventional and existing “fast” bilateral filter implementations.