Hyperbolic Tangent Sigmoid as a Transformation Function for Image Contrast Enhancement
Laritza Pérez–Enríquez, Saul Zapotecas Martinez, Diego A. Oliva, Leopoldo Altamirano-Robles · 2023
Contrast enhancement is critical for investigating and highlighting important hidden features in a computer vision system. Continuous functions, such as incomplete beta or sigmoid functions, have traditionally been used for histogram equalization. However, histogram equalization cannot uniformly enhance the local contrast of an image, which is its main limitation. In this study, we investigate a contrast enhancement method based on a hyperbolic tangent sigmoid whose parameters can be optimized by metaheuristics. In our study, we investigated the performance of three popular metaheuristics when coupling the proposed hyperbolic tangent sigmoid to find the optimal pixel values that can intensify features of low-contrast images. The proposed method is studied on a public domain image dataset and evaluated using standard performance indicators. Preliminary results show that the proposed hyperbolic tangent sigmoid can improve image contrast and quickly adapt to other metaheuristics.