A New Class of Generalized Morphological Hybrid Filters
一类新的广义形态学混合滤波器

Traditional morphological filters perform poorly in reducing noise, and the grey values of the images filtered by them have a heavy deviation. Although the morphological hybrid filters can eliminate the deviation, they require much more operations and time. A new class of generalized hybrid alternating sequence filters (GHASF) is proposed, which can inherit the important properties of the general hybrid alternating sequence filters, maintain a strong symmetry of the dual filters, and have less than half computational complexity. Simulation results show that the new hybrid filters can not only effectively remove the Gaussian noise in the image, but also preserve the edge details. Besides, the images filtered may have a higher peak signal-to-noise ratio and a smaller root mean square error. Compared with the general hybrid morphological filters, on the premise of the peak signal-to-noise ratio, the generalized morphology hybrid filters can improve the processing speed by more than twice, on the premise of the peak signal-to-noise ratio (PSNR).