Restoring degraded underwater images is a challenging ill-posed problem. Existing priors-based approaches have limited performance in many situations due to their hand-designed features. In this paper, we propose an effective convolutional neural network (CNN) based approach for underwater image restoration, which consists of a transmission estimation network (T-network) and a global ambient light estimation network (A-network). By learning the relationship between the underwater scenes and their corresponding blue channel transmission map and global ambient light respectively, we can recover and enhance the underwater images with an underwater optical imaging model. In T-network, we use cross-layer connection and multi-scale estimation to prevent halo artifacts and to preserve edge features. Moreover, we develop a new underwater image synthetic method for training, which can simulate underwater images captured in various underwater environments. Experimental results of synthetic and real images demonstrate that our restored underwater images exhibits more natural color correction and better visibility improvement against these state-of-the-art methods.