bio-inspired computing paradigm for periodic‎ noise reduction in digital images

Periodic noise reduction is a fundamental problem in image processing, which severely affects the visual quality and subsequent application of the data. most of the conventional approaches are only dedicated to either the frequency or spatial domain. in this research, we propose a dual-domain approach by converting the periodic noise reduction task into an image decomposition problem. we introduced a bio-inspired computational model to separate the original image from the noise pattern without having any a priori knowledge about its structure or statistics. experiments on both synthetic and non-synthetic noisy images have been carried out to validate the effectiveness and efficiency of the proposed algorithm. the simulation results demonstrate the effectiveness of the proposed method both qualitatively and quantitatively.