An Effective Hybrid Filter for the Removal of Gaussian-Impulsive Noise in Computed Tomography images

Computed tomography images can be corrupted by mixed noises such as Gaussian and impulsive noise during acquisition time which results in reduction of its quality. Hence removing the noise from the image is very significant in medical image processing. The existing filters such as mean and median filter are not that efficient in removing impulse and Gaussian noise by retaining the details of the image. In this paper, a new filter is proposed which removes mixed noise such as Gaussian and impulse noise. Initially, pixels of image are separated into non-corrupted pixels and corrupted pixels based on existence of noises in their small neighborhood. The greyscale value of non corrupted pixels are taken as output directly and for the corrupted pixels, removing Gaussian noises and impulse noises respectively is done based on their characteristics. The results demonstrate that the proposed filter can eliminate mixed noise of different density in a better way by also preserving the details of image when compared with the mean filter or the median filter for mixed noise.