Paper:
Single Image Super Resolution (SISR) is the task of producing a high resolution (HR) image from a given low-resolution (LR) image. It is a well researched problem with extensive commercial applications like digital camera, video compression, medical imaging, etc. Most recent super resolution works focus on the feature learning architecture, like Chao Dong (2016); Wang et al. (2018b); Ledig et al. (2017). However, these works suffer from the following challenges: (1) The low-resolution (LR) training images are artificially synthesized using HR images with bicubic downsampling, which have much more information than real demosaic-upscaled images. The mismatch between training and realistic mobile data heavily blocks the effect on practical SR problem. (2) These methods cannot effectively handle the blind distortions during super resolution in practical applications. In this work, an end-to-end novel framework, including high-to-low network and low-to-high network, is proposed to solve the above problems with dual Generative Adversarial Networks (GAN). First, the above mismatch problems are well explored with the high-to-low network, where clear high-resolution image and the corresponding realistic low-resolution image pairs can be generated. With high-to-low network, a large-scale General Mobile Super Resolution Dataset, GMSR, is proposed, which can be utilized for training or as a benchmark for super resolution methods. Second, an effective low-to-high network (super resolution network) is proposed in the framework. Benefiting from the GMSR dataset and novel training strategies, the proposed super resolution model can effectively handle detail recovery and denoising at the same time.

Review:
In the paper, the authors proposed an end-to-end single image super-resolution framework, which is composed of two parts. First, a high-to-low network is trained to generate realistic HR/LR image pairs for training super-resolution models. Using this network, the authors designed a large-scale General Mobile Super Resolution Dataset, GMSR, which can be utilized for training or as a benchmark for super-resolution methods. Second, a low-to-high network is trained to produce a super-resolution image. Beneﬁting from the GMSR dataset and novel training strategies, the proposed low-to-high network could thus deal with the real distortions and restore ﬁne details.
The paper is easy to follow. The proposed framework is logical and the experiments proved to be effective. However, there are some problems as follows:
1. In the high-to-low network of framework, Gaussian random noise is added to clean high-resolution images to simulate the randomness of the distortions in LR. Why can do this in this way? The rationality of this setting should be given. Are there other kinds of noises be applied? As far as I knew, the in-camera processing pipeline will involve  Poisson-Gaussian noise rather than Gaussian only and other factors such as demosaicing, Gamma correction, and JPEG compression. It is hard to only consider Gaussian noise to stimulate the whole in-camera process. 
2. The experimental part is remarkable insufficient to an ICLR paper. There only have a quantitative evaluation of the proposed framework with other super-resolution algorithms, while lacking the qualitative evaluation of the framework itself. 
3. There are some formatting issues in the paper,
   1. Page 8, figure 5, here some errors in the label color of the figure.
   2. Page 1, introduction, line 14, "However, In most practical..." -> "However, in most practical...";
   3. Page 2, line 10, "...many problems, such as..., which is hard to avoid" -> "...many problems, such as..., which are hard to avoid";
   4. The last paragraph of the introduction, line 6, "...parameters.Third,..." -> "...parameters. Third,..."
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