Underwater resource exploration is a need for human development, but discovering of resources manually is not possible due to complex aquatic environment, effective methods for exploring underwater ecology is underwater image enhancement, this paper provides information about various techniques used for UIE. Conventional image processing techniques do not deal with the issues related to light scattering and light deviation which leads to degraded image and not effective for turbid water images. Advancement in deep learning techniques such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) are used to enhance the images with good resolution and also addresses the issues related to distortion, noise and blurs in the images. In this paper, importance is also given to many evaluation metrics for gauging the quality of amplified underwater images. This work aim is to give a thorough analysis of various traditional and deep learning techniques used for underwater image enhancement.