Authors : P. Roji Chanu and Kh. Manglem Singh
Abstract: This study presents a novel two-stage filtering algorithm for removing impulse noise in color images. Quaternion theory is used to represent the intensity and chromaticity differences of two color pixels. Use of quaternion treats color pixels as vectors and processes color images as single unit rather than as separated color components. This preserves the existing correlation and three dimensional vector natures of the color channels. In the first stage of noise detection, the color pixels are sorted and assigned a rank based on the aggregated sum of color pixel differences with other pixels inside the filtering window. The central pixel is considered as probably corrupted by an impulse if its rank is bigger than a predefined rank. In the second stage, the probably corrupted candidate is again checked for an edge or an impulse by using four Laplacian convolution kernels. If the minimum difference of these four convolution is larger than a predefined threshold, then the central pixel is regarded as an impulse. The noisy pixel is replaced by output of weighted vector median filter implemented using the quaternion distance. More weight is assigned to those pixels belonging to the direction of minimum difference. Experimental results indicate the improved performance of the proposed filter in suppressing the impulse noise while retaining the original image details comparing against other well-known filters.
P. Roji Chanu and Kh. Manglem Singh, 2020. Two-Stage Quaternion Vector Median Filter for Removing Impulse Noise in Color Images. Journal of Engineering and Applied Sciences, 15: 350-364.