Enhancing underwater images is crucial for various deep-sea applications, such as underwater object tracking, detection, seabed exploration, and military security. However, underwater images often suffer from low contrast, haze, poor feature details, and color distortion due to light scattering and absorption in water. While several state-of-the-art methods have attempted to address these issues, they often struggle with challenging underwater scenes, resulting in residual haze, low contrast, unwanted color tones, and poor feature clarity. To address these challenges, we propose a novel method for contrast and feature enhancement. This adaptive technique processes each color channel to balance contrast and improve feature details by stretching both the foreground and background of the input image. The proposed method incorporates genetic optimization and unsharp masking to first enhance contrast, followed by histogram stretching to improve feature details. Extensive qualitative and quantitative experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed approach in producing high-quality underwater images. The EUVP [1] and UCCS [2] datasets are used for qualitative analysis of the proposed approach. The proposed model demonstrates superior performance compared to state-of-the-art methods. Histogram equalization effectively illustrates the contrast and color levels in the images enhanced by the proposed model. The quantitative evaluation is conducted using UIQM, UCIQE, and EI metrics, which assess the model's effectiveness. Achieving the highest scores across these metrics, the model exhibits significant improvements in contrast, color, and texture details in the enhanced images.