In real-world underwater environment, exploration of seabed resources, underwater archaeology, and underwater fishing rely on a variety of sensors, vision sensor is the most important one due to its high information content, non-intrusive, and passive nature. However, wavelength-dependent light attenuation and back-scattering result in color distortion and haze effect, which degrade the visibility of images. To address this problem, firstly, we proposed an unsupervised generative adversarial network (GAN) for generating realistic underwater images (color distortion and haze effect) from in-air image and depth map pairs based on improved underwater imaging model. Secondly, U-Net, which is trained efficiently using synthetic underwater dataset, is adopted for color restoration and dehazing. Our model directly reconstructs underwater clear images using end-to-end autoencoder networks, while maintaining scene content structural similarity. The results obtained by our method were compared with existing methods qualitatively and quantitatively. Experimental results obtained by the proposed model demonstrate well performance on open real-world underwater datasets, and the processing speed can reach up to 125FPS running on one NVIDIA 1060 GPU.
In this article, the authors propose a generative adversarial network named UWGAN to generate realistic underwater images from the pairs of in-air images and depth images. Then, a U-Net was leveraged to enhance the results. 
However, the text suffers from too many language problems. The authors should consult professional proofreading services. As a courtesy towards referees, the quality of writing needs meticulous attention before a scientific paper should be submitted. 
	Other comments:
1.	The literature is limited. I found some novel works being done in the field that must be addressed and listed in the background and experiments.
2.	The underwater imaging model presented in this paper derives from the Jaffe-McGlamery model, which is a common sense in this field. The authors use a generator to produce underwater images that only implements the common model by a neural network. Moreover, the statement of section 2.2 is not clear. Please rewrite this section.
3.	The authors used U-Net without any improvement to enhance the results generated from UWGAN, which is the integration of existing models. 
4.	The authors claimed that their model is better than others, while there is no evidence to indicates that. For example, 1) in (page 5, line 4 from bottom), “It can be seen that our proposed method has achieved a higher score.”, can we observe this from the Table 1 and 2? 2) “The method we proposed has the fastest processing speed compared to other methods. Moreover, the method proposed in this paper has the fewest parameters compared to other deep-learning-based methods.”, it is suggested that a study about the parameters and FLOPs of the involved methods should be given.
5.	Please carefully check the references. For example, “Hummel R. Image enhancement by histogram transformation[J]. Unknown, 1975.” lacks the journal name.
6.	High-resolution figures should be given in the manuscript.