A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

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.

Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement

“…It is used to determine the optimal parameter values of a transformation function T towards improving the performance of an MIE technique. In this study, we used the evaluation function proposed in [11]. It was selected for the following reasons: First, it integrates four different key metrics, thus making it robust compared to other methods.…”

“…Each parameter is constrained within certain bounds, and the following bounds were used: 2 ≤ a ≤ 2.5; 0.3 ≤ b ≤ 0.5; 0 ≤ c ≤ 3; and 3 ≤ k ≤ 4, which were obtained from extensive experiments conducted in [11]. Other common inputs already well known in the literature are the evaluation function E, population size P , and the maximum fitness computation rate F CR max .…”

“…Owing to the subjective nature of the image enhancement process, several metaheuristic optimization algorithms (MOA) have found great application for image enhancement and with remarkable results to show [20]. The use of MOAs for MIE purposes has been demonstrated several times in the literature to outperform the existing methods mentioned above [11], [20]. Consequently, the literature has literally exploded with a long list of different MOAs, with many being deployed for MIE functions [21].…”

“…By using the FCR, we can ensure that the objective function is computed the same number of times for all MOAs, thus guaranteeing that the computational resources of the MIE machine are consumed almost equally by all MOAs. Next, we deployed the transformation function proposed in [20] coupled with a robust evaluation function used in [11] to serve as the objective function for MIE purposes. Finally, a number of performance parameters were used to analyze the different MOAs in the qualitative and quantitative sense.…”

Weak‐Light Image Enhancement Method Based on Adaptive Local Gamma Transform and Color Compensation

“…Weak-light image enhancement has become a focus of research in the image processing field, and its interdisciplinary characteristics have attracted considerable attention from researchers worldwide. For example, in a facial recognition system, Oloyede et al [6] applied a new evaluation function in conjunction with metaheuristic-based optimization algorithms to automatically select the best-enhanced face image. To enhance underwater images, Hou et al [7] presented a novel underwater color image enhancement approach based on hue preservation by combining the HSI and HSV color models.…”