Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: 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.

review_title: Official Blind Review #3

final_score: rating: 3: Weak Reject
review_content: This paper uses U-net for underwater image restoration and enhancement. But, it is difficult to obtain realistic underwater images, thus this paper introduces a GAN-based method to generate realistic underwater images from in-air image and depth map pairs.

- Although this paper points out that the previous work (i.e. WaterGAN) generates color noise and the camera model is not suitable, how does this proposed method overcome these points? Please make it clear.

- The figures in this paper are too blurry to see them. To evaluate the effectiveness of the proposed method, the figures are important, thus, it would be better to make them clear.

- The technical contribution of the proposed method is not clear. The proposed method seems to be just using the existing techniques.
review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: [Update after rebuttal period]
In response, the authors cannot clearly clarify the difference between this work with existing works integrating the physical model into the network. Thus I stay my original score.


[Original reviews]
This paper proposed an unsupervised generative adversarial network for underwater generating realistic underwater images and haze removal, which can simultaneously deal with the color restoration and haze in the realistic underwater environment.

Firstly, according to the widely used physical model in the image processing area, employed the UnderwaterGAN to trained parameters in advanced, and then use U-Net for color restoration and haze removal of underwater images. However, many existing works used the physical model to represent the imaging principles and using deep network to learn prior knowledge. Thus, I think the proposed idea is a little bit incremental.

For the experimental part, the experimental results fully demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. Additionally, the ablation studies in the appendix also give us the intuition by using the different loss functions. Also, I suggest the authors demonstrate the proposed method on not only low-level, but also high-level vision tasks, e.g., underwater image target detection. 

Finally, the paper is well organized and sentence expression is also clear, but small errors that are correctable. 

review_title: Official Blind Review #1

Meta-review: This paper proposed to improve the quality of underwater images, specifically color distortion and haze effect, by an unsupervised generative adversarial network (GAN). An end-to-end autoencoder network is used to demonstrate its effectiveness in comparing to existing works, while maintaining scene content structural similarity. Three reviewers unanimously rated weak rejection. The major concerns include unclear difference with respect to the existing works, incremental contribution, low quality of figures, low quality of writing, etc. The authors respond to Reviewers’ concerns but did not change the rating. The ACs concur the concerns and the paper can not be accepted at its current state.
Decision: Reject