Abstract:
Image enhancement is an integral component of face recognition systems and other image processing tasks such as
in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based
approaches have gained popularity owing to their highly effective performance rates. However, the need for improved
evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods.
Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image
enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based
optimization algorithms in order to select automatically the best enhanced face image based on a linear combination
of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation
function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better
suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with
existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the
qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained
environments significantly.
