Robust anisotropic diffusion filter via robust spatial gradient estimation

This paper aims to develop a robust anisotropic diffusion filter associated with a robust spatial gradient estimator for simultaneously removing the additive white Gaussian noise (AWGN) and impulsive noise. A robust spatial gradient estimator is first developed to more effectively achieve the separation of significant features and noise. This technique rejects the impulsive noise in the spatial domain and the small amplitude noise in the frequency domain while keeping the large amplitude gradient in the spatial domain and the medium amplitude wave in the frequency domain, and therefore the estimated spatial gradient can mask out various types of noise such as the additive white Gaussian noise and impulsive noise. Then, the spatial gradient obtained from the robust spatial gradient estimator is incorporated into the diffusivity function to obtain the desired robust anisotropic diffusion filter and the MAD estimator is further proposed to estimate the diffusion threshold under such circumstance. Experimental results indicate that the proposed filter remarkably outperforms some benchmark robust models with regard to the quantitative metrics and visual performance.

Automated summary

This paper proposes a robust anisotropic diffusion filter that can simultaneously remove additive white Gaussian noise and impulsive noise, using a robust spatial gradient estimator to separate features and noise. Experimental results demonstrate that the proposed filter outperforms benchmark models in both quantitative metrics and visual performance.