Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement

Abstract

Nature-inspired algorithms are widely applied in the arena of image enhancement for various optimization purposes. To address the optimization complexities in various image enhancement approaches, nature-inspired optimization algorithms play a vital role. Cuckoo search is one of the prominent nature-inspired performance algorithms that we employed in this work for the enhancement of magnetic resonance imaging (MRI). We proposed a wavelet-based masking technique employing a cuckoo search algorithm whose masking value is corrected by gamma function for the contrast enhancement of MRI images. The cuckoo search algorithm can inevitably fine-tune the relation of nest building using genetic operatives like adaptive cusp and alteration. The proposed contrast enhancement scheme is examined quantitatively for different types of MRI images. Extensive simulation results compared with quantitative values have revealed that the traditional nest building of cuckoo search optimization is improved by adaptive gamma correction. Comparative analysis with the existing works establishes the usefulness of the proposed methodology over the other standard approaches.

Joint inversion for magnetotelluric data based on cuckoo search algorithm and least squares method

“…Cuckoo search (CS) algorithm is a novel intelligent optimization algorithm proposed by Yang [13], which is inspired by the parasitic breeding habit of cuckoos, and it adopts the Lévy ight mode to update the individuals, which can effectively jump out of the local optimum. With the advantages of few parameters, simple operation, fast convergence and strong global optimization ability, the CS algorithm has been successfully applied to a variety of elds, such as multi-objective optimization [14,15], image processing [16,17], resource allocation [18,19], control problems [20,21], and computer vision [22].…”