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The Gaussian and impulse noises corrupt the Computed Tomography (CT) images either individually or collectively, and the conventional fixed filters do not have the potential to suppress these noise.

These spurious noises affect the inherent features of CT image awkwardly. Hence, to handle such a situation adaptive Cat Swarm Optimization based Functional Link Multilayer Perceptron (CSO-FLMLP) has been proposed in this paper to get rid of unwanted noise from the CT images.

Here, the nature-inspired CSO technique which is an optimization algorithm has been employed to assist in updating the weights of FLMLP network. In this work, the cost function considered for CSO is the error between noisy and contextual pixels of reference images which need to minimize. For examining the efficiency of CSO-FLMLP filter, it is compared with the other six competitive adaptive filters …