An impulsive noise color image filter using learning-based color morphological operations

Morphological filter is a kind of morphological operation-based nonlinear filter. It is effective in impulsive noise filtering and has been extensively studied in the past two decades. In the presented study, a new multichannel filtering approach established on learning-based color morphological operations for impulsive noise removal in color image is presented. By using the color pixel ordering scheme learned from the pre-estimation of impulsive noise, contaminated pixels are ordered as maximum ones in erosion operation or minimum ones in dilation operation, respectively. This characteristic ensures that only uncontaminated color pixels are distributed in morphological operations, hence noisy pixels are suppressed. Reconstruction is followed to alleviate the blurring and bias effects of morphological operations and to preserve image features. The presented filtering approach greatly enhances the performance of morphological operation-based filters, especially in the color image highly corrupted by impulsive noise. Experiments and comparisons with classical filters, such as basic vector median filter (VMF), basic vector directional filter (BVDF), NOPNCP filter, etc., as well as some newly developed filters, are performed to demonstrate the effectiveness of the proposed color image filtering algorithm.