Non-local image filter algorithm based on adaptive parameter regression

For additive white Gaussian noise of an image, this paper proposes an optimized adaptive parameter filter algorithm. Based on the non-local Euclidean medians (NLEM) algorithm, according to the property that the noise image gradient amplitude obeys Rayleigh distribution within a certain noise range, we obtain a binary adaptive filter parameter by regarding gradient amplitude and noise standard deviation as independent variables. The adaptive filter parameter is introduced in the weight calculation of neighbors. Furthermore, the changes of the noise affect the selections of the l_p norm regression. Make p used polynomial fit with noise standard deviation in a certain range, and get adaptive l_p norm regression. On the basis of adaptive filter parameters, l₁ norm regression used in NLEM can be improved by using adaptive l_p norm regression. It is verified that the new algorithm not only obtains satisfactory results of denoising in a certain noise range, but also reduces the degree of human-computer interaction effectively.