Underwater image enhancement is an active area of research due to its wide applications in areas like marine research, automated underwater vehicles, etc. In general, the underwater images have low contrast, blurriness, and color cast due to various effects like absorption, scattering, and refraction. Normally, the underwater images are less clear and not suitable for various applications. The underwater images should be enhanced to use for real-life applications and the natural image enhancement techniques may not work well for underwater images. In this paper, we introduce a scheme for enhancing the quality of the underwater images by using a residual neural network (ResNet). The synthetic underwater images for the experimental study are generated using the underwater generative adversarial network (UWGAN). The experimental results show that the new underwater image enhancement techniques perform well, and it can be used for real-life applications where we need good quality underwater images.