This study proposes a conditional generative adversarial network that improves the performance of underwater image enhancement of different colors. The network adds residual module in residual dense blocks into the generative model, and its dense cascade and residual connections extract image features and ease the gradient disappearance problem. By adding two new loss functions to the objective function, a new network model is established which can make the content and structure of the enhanced images be consistent with that of the input images. The experimental results show that the proposed method has better enhancement performance and visual effect than existing algorithms.