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 the subsequent application of the data . Most of the conventional approaches are only dedicated to either the frequency or the spatial domain . In this research work , we propose a dual-domain approach by converting the periodic noise reduction task into an image decomposition problem . We introduce 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 the synthetic and non-synthetic noisy images are carried out in order to validate the effectiveness and efficiency of the proposed algorithm . The obtained results demonstrate the effectiveness of the proposed method both qualitatively and quantitatively .