A hybrid de-nosing technique for multi-noise removal on gray scale medical images

Abstract

Image De-noising is a subfield of image enhancement in general, that it focuses on the removal of any undesired details that corrupts the digital image. Actually, this can be achieved through various filtering techniques, where the variation is compared on a base of enhancement parameters and the keep of sensitive and important details. This paper Propose a novel hybrid de-noising approach that is based on adaptive median filter in the spatial domain followed by wiener filter in the Fourier transform domain for the removal of circular blurredness, Gaussian and impulse additive noises simultaneously. The proposed de-nosing approach is tested on a data set of gray scale medical images or Digital Imaging and Communications in Medicine (DICOM), each of which was corrupted by additive Gaussian noise with variance 0.05 and slat & pepper with probability 0.2. Moreover, the paper analyzes the hybrid de-noising approach in terms of peak signal to noise ratio (PSNR) for image quality assessment. The results showed that the proposed hybrid approach recorded higher PSNR 19.8 dB compared to the standalone adaptive median or wiener filters.