3-D nonlocal means filter with noise estimation for hyperspectral imagery denoising

Noise reduction is one of important processing tasks for hyperspectral imagery (HSI). In this paper, a three-dimensional (3-D) nonlocal means filter is proposed for noise reduction of HSI. Recently, non-local means method attracts many attentions due to its global and local integrated property. Nonlocal algorithm searches the similar image patches in the whole scene to build the mean filter, so that it overcomes the disadvantage of local filter that only local pixels within a small neighbor is used, and the disadvantage of global filter that local structure is ignored. In order to explore the spectral-spatial correlation of HSI, nonlocal means method is extended from 2-D to 3-D. Furthermore, as HSI contains both of signal-independent and signal-dependent noises, variance-stabilizing transformation based on noise estimation is used to make noise reduction under the additive Gaussian noise model. Experiments with the real hyperspectral data set indicate that the proposed strategy can work well in both of detail preservation and noise removal.