Enhancing the visual clarity of underwater images is a significant challenge due to the adverse impacts of water and lighting conditions. Color imbalances, low contrast, and limited visibility of details can hinder the interpretation of underwater images. The proposed approach employs a fusion strategy characterized by multiple weights and granularity levels, effectively integrating diverse image processing methods to tackle the unique constraints inherent to underwater imaging. The core of our method involves addressing color imbalances through precise white balance correction applied to input underwater images. Notably, adaptive histogram equalization with limited contrast is anticipated to play a pivotal role in enhancing image contrast while preserving essential features. A critical phase of this investigation entails evaluating the proposed technique using established quality metrics like UIConM, UIQM, and UCIQE. These metrics are poised to provide valuable insights into the efficacy of the technique in elevating the quality of underwater images. By elevating image quality and enhancing clarity, the proposed approach is poised to facilitate more precise analysis and informed decision-making within the challenging context of underwater environments.