Optimal soft morphological filter for periodic noise removal using a particle swarm optimiser with passive congregation

Periodic noise widely appears in raw images and is often accompanied with white noise. The removal of such compound noise is a challenging problem in image processing. To avoid using the time-consuming methods such as Fourier transform, a simple and efficient spatial filter called optimal soft morphological filter (OSMF) is proposed in this paper. The filter is a combination of basic soft morphological operators and the combination parameters are optimised by an improved particle swarm optimiser with passive congregation (PSOPC) subject to the least mean square error criterion. Applying OSMF to the removal of periodic noise with different frequencies, the simulation results are analysed in comparison with spectral median filter (SMF), which show that OSMF is more effective and less time-consuming in reducing both pure periodic and compound noise meanwhile preserving the details of the original image.