High-quality deep-sea images are essential for the development of marine biology, topography and geology etc. In order to solve the problems of color distortion, image blur and low contrast in deep-sea images, we propose a deep-sea image enhancement network using improved U2-Net as the GAN generator. Firstly, the RSU module is introduced in U-Net to enhance the fusion of high-level and low-level information in the network. Secondly, the DA mechanism is introduced in the skip connection of U2-Net, which is used to enhance the interrelationship between the space and channel of the image, and extract the underwater color and texture details. Then, U2-Net with the DA mechanism, is used as the generator of GAN to enhance the realism of the image in the adversity. In addition, a new loss function with edge loss and perceived loss is reconstructed, called DS-Loss and the mapping relationship between deep-sea images and target images of U2-GAN is guided by DS-Loss from multiple perspectives. Finally, U2-GAN is compared with seven advanced underwater image enhancement algorithms on the self-built dataset DSIED. Compared with the second-place Sea-Pix-GAN, U2-Net improves by 5.6%, 3.9%, 5.2%, 16.0%, 7.1% and 2.4% in PSNR, SSIM, IE, UIQM, UCIQE, and PCQI, demonstrating better underwater image enhancement effects.