Filter Parameter Estimation in Non-Local Means Algorithm

In this paper, improvements to the Non-local Means (NL-Means) algorithm introduced by Buades et al. are presented. The filtering parameter is unclearly defined in the original NL-Means algorithm. In order to solve this problem, we calculated filtering parameter by the relation of noise variance, and then proposed a noise variance estimate method. In this paper, noisy image is transformed by wavelet. The wavelet coefficients in each sub-band can be well modelized by a Generalized Gaussian Distribution (GGD) whose parameters can be used to estimate noise variance. The simulation results show that the noise variance estimate method is not only exact but also makes the algorithm adaptive. The adaptive NL-Means algorithm can obtain approximately optimal value, and need less computing time.