Enhancing image quality is crucial for achieving an accurate and reliable image analysis in vision-based automated tasks. Underwater imaging encounters several challenges that can negatively impact image quality, including limited visibility, color distortion, contrast sensitivity issues, and blurriness. Among these, depending on how the water filters out the different light colors at different depths, the color distortion results in a loss of color information and a blue or green tint to the overall image, making it difficult to identify different underwater organisms or structures accurately. Improved underwater image quality can be crucial in marine biology, oceanography, and oceanic exploration. Therefore, this paper proposes a novel Generative Adversarial Network (GAN) architecture for underwater image enhancement, restoring good perceptual quality to obtain a more precise and detailed image. The effectiveness of the proposed method is evaluated on the EUVP dataset, which comprises underwater image samples of various visibility conditions, achieving remarkable results. Moreover, the trained network is run on the RPi4B as an embedded system to measure the time required to enhance the images with limited computational resources, simulating a practical underwater investigation setting. The outcome demonstrates the presented method applicability in real-world underwater exploration scenarios.