MONTE CARLO NON-LOCAL MEANS METHOD FOR HYPERSPECTRAL IMAGE DENOISING

Hyperspectral image (HSI) denoising has become an important research topic in the research community due to its significance improvements in many applications (e.g. classification). In this paper, we introduce a Monte Carlo non-local means (MCNLM) method for noise reduction of the HSI. Each band of the HSI is processed by the MCNLM, which is a randomized algorithm suitable for large-scale patch-based image (e.g. HSI) filtering. More specifically, the MCNLM is achieved by randomly choosing a fraction of the similarity weights to obtain an approximated result. Compared to the classical non-local means (NLM), the MCNLM consumes less time while achieves comparable performance. Experimental results on the real hyperspectral data set demonstrate the promising performance of the MCNLM for HSI denoising.