Underwater image enhancement is an important process in image processing due to the images often suffering from severe degradation causes by the nature of light and underwater environment. The purpose of this research is to study the existing methods and algorithms for enhancing underwater images. In this paper, we compared 3 different deep learning-based methods (i.e. Water-Net, Shallow-UWnet, Deep Learning and Image Formation Model) for underwater image enhancement. Furthermore, we proposed an enhancement method based on white balance, adaptive gamma correction, sharpening and multi-scale fusion technique. The result of our proposed method is fed into the deep learning-based models. A real-world dataset which is the Underwater Image Enhancement Benchmark (UIEB) dataset is used for the model training and testing. Experimental results show that our proposed method improves the colour hue, image clarity and achieves higher scores in terms PSNR, SSIM and UIQM metrics.