We propose a stochastic bilateral filter (SBF) and stochastic nonlocal means filter (SNLM)—fast image filtering aimed at processing high dimensional images (such as color and hyperspectral images). SBF and SNLM are comprised of an efficient randomized process, where it agrees with conventional bilateral filter (BF) and nonlocal means filter (NLM) 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/NLM output. The computational bottleneck of the SBF is constant with respect to the window size and color dimension, meaning the complexity for filtering hyperspectral images is nearly the same as the grayscale images. The computational bottleneck of the SNLM is constant with respect to the window size and the image patch size. They are considerably faster than the conventional and existing “fast” BF and NLM implementations. [Reference Code]
Stochastic Bilateral Filter For High-Dimensional Images Inproceedings
In: Image Processing, 2015. ICIP 2015. IEEE International Conference on, IEEE 2015.