Underwater image enhancement is a subject of significant interest due to its wide-ranging applications across various domains. However, underwater imaging faces numerous challenges including poor visibility, color distortion, and loss of image details. In this paper, we try to resolve these problems using a novel ensemble loss function comprising three new loss terms - gradient loss, contrast loss, and brightness loss. These novel loss functions are specifically designed to address the unique challenges associated with underwater imaging to enhance image quality, instead of using conventional perceptual loss. The proposed methodology is evaluated using the EUVP (Enhanced Underwater Vision Benchmarking) dataset, a widely adopted benchmark dataset in the field of underwater image enhancement. Experimental results demonstrate significant improvements in the enhanced images when incorporating the proposed loss functions alongside the conventional losses. These enhancements include improved fidelity, enhanced colour correctness, and reduced artefacts, leading to an overall enhancement of the visual quality of underwater images. Furthermore, the performance of the proposed approach is compared against existing methods, showcasing its superiority in terms of image quality, fidelity and visual perception. The results affirm the effectiveness and robustness of the proposed methodology in addressing the challenges inherent in underwater image enhancement.