Noise removal from images is an important part of images pre treatment

and its result has a great effect on the final result of images processing. How to take full advantage of local characteristics and details in images to improve noise removal

detail preserving and edge preserving ability of filters? A nonlinear filtering algorithm using probability statistic and main texture direction analysis is proposed.This algorithm utilizes Radon transform to determine texture direction probability density distributions of local areas of images and then applies probability statistic model to estimate the middle pixel’s gray value according to its neighbour pixels. The performance of the proposed method is evaluated with several sets of images contaminated by pulse noise and Gaussian noise. The experimental results show the superiority of this method including the ability of de noising and preserving edges and details of images especially for images with pulse noise and Gaussian noise. This algorithm makes richly use of local characteristics and details of images. Especially unlike some recent algorithms applying only for salt and pepper impulse or Gaussian noise removal

our method

is applicable to images contaminated by any kind of noise.