Contrast enhancement techniques play a vital role in image processing by amplifying features, brightness, and luminosity to create images tailored for diverse research applications. Addressing specific tasks in various domains necessitates an in-depth exploration of contrast enhancement, especially in computer vision systems where uncovering hidden features is crucial. This chapter delves into a contrast enhancement approach utilizing the Gauss error function, whose parameters can be fine-tuned through metaheuristics. Investigating the synergy of the Gauss error function with three widely recognized metaheuristics aims to optimize pixel values, enhancing features in low-contrast images effectively. Leveraging a dataset of public domain images, the presented method undergoes rigorous evaluation using standard performance indicators to measure the improvements in terms of contrast. Preliminary findings indicate that the Gauss error function enhances image contrast and exhibits rapid adaptability to various metaheuristics. The investigation presented in this chapter contributes to the advancement of contrast enhancement methodologies, offering a promising approach to enhance image quality in various computer vision applications.