Paper:
Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison. The basic results are conforming with the most of the common notions in machine learning, and also introduces some novel heuristics and recommendations on noise injection. The new approaches will provide better understanding on optimal learning procedures for image classification.

Review:
This paper studies the different types of noises that could be added to the training image dataset while training an CNN model for classification. They study 5 different types of noise functions: Gaussian, Speckle, Salt and Pepper, Poisson, Occlusion.
 
Pros:
1. Rigorously studying how to augment training data for CNN is important. 
Cons:
1. The primary question is novelty . - what is the research contribution of this dataset? They are running experiments of 5 different known noise functions, on two image datasets, on a single deep learning models. There are no fundamental research questions or hypothesis. This is a mere running of few experiments - known methods and known approaches. 
2. Are the results generalizable? Answer is no! The results on shown on two subsamples of Imagenet datasets for only ResNet 18 model. Maybe for this combination speckle noise (and not Gaussian, as pointed out in the comments by the authors) is better. How can the assure that for a different dataset, model, task combination the same speckle noise would perform better ? 
3. Improvement suggestion: What I would ideally look in this topic, is a method to automatically study the properties of the training data images (study the distribution) and conditional on this distribution recommend the best noise type and noise intensity. Thus, the whole story of noise injection could be made dynamic for a dataset, model, task combination
4. Writing of the paper could be improved: 1. The need for noise based augmentation of is well known (Section 1). 2. The different kinds of noise functions are mostly text book knowledge (Section 2). 3. The different image quality metrics written here - MSE, PSNR, SSIM are also text book knowledge (Section 3). Overall, the first 4 pages of the paper are redundant and could be compressed into 1 page. Would like to read more on the experiments, analysis, and maybe automation of noise selection techniques in different kinds of tasks - segmentation, text classification, seq2seq etc.
5. Choose very naive noise (or augmentation) functions: In general, the approach of augmentation of training data has evolved so much in the literature, that adding noise is hardly in practice. 
1. "The Effectiveness of Data Augmentation in Image Classification using Deep Learning" - Style Transformation
2. "Improving Deep Learning using Generic Data Augmentation" - Geometric and Affine Transformation
Thus, the study on data augmentation should be performed across all these different transformation functions on training data and using only noise function is naive and is incomplete.