  Contrast enhancement is very important in terms of assessing images in an
objective way. Contrast enhancement is also significant for various algorithms
including supervised and unsupervised algorithms for accurate classification of
samples. Some contrast enhancement algorithms solve this problem by addressing
the low contrast issue. Mean and variance based sub-image histogram
equalization (MVSIHE) algorithm is one of these contrast enhancements methods
proposed in the literature. It has different parameters which need to be tuned
in order to achieve optimum results. With this motivation, in this study, we
employed one of the most recent optimization algorithms, namely, coot
optimization algorithm (COA) for selecting appropriate parameters for the
MVSIHE algorithm. Blind/referenceless image spatial quality evaluator (BRISQUE)
and natural image quality evaluator (NIQE) metrics are used for evaluating
fitness of the coot swarm population. The results show that the proposed method
can be used in the field of biomedical image processing.
