A new denoising algorithm for random valued impulse noise in images using measures of dispersion
Neeti Singh, O. Uma Maheswari · 2017
This paper proposes a novel image Denoising algorithm which accurately detects noisy pixels in images corrupted by Random Valued Impulse Noise at very high noise levels i.e. up to 95% noise density. It uses two levels of thresholds to adequately address the drawbacks of existing methods, specially the misdetection of noisy pixels as noise free pixels and vice versa. Measures of Dispersion such as mean, Standard Deviation and Quartile have been used to define Thresholds. After the detection, a Fuzzy Switching Weighted Median Filter is applied to restore the corrupted image very close to the original image. As confirmed by the simulation results, the proposed method is superior to the existing methods in detection.