Response to Reviewer 1

General Comments:

“Under the background that deep learning has excellent characteristics in the field of single image super-resolution (SR) (SISR) and is widely used, a SISR method guided by image quality assessment (IQA) is proposed based on deep learning structure, in order to balance the perceived quality and distortion measurement corresponding to SISR results. In this method, IQA network is introduced to extract the perceptual features in SR, and the corresponding loss fused with the original absolute pixel loss is calculated to guide the adjustment of SR network parameters. In addition, an interactive training model is constructed by cascaded network, and based on the problem of insufficient samples in the training process, the hinge loss method of pairwise sorting is proposed. Combined with the evaluation results of benchmark data sets, it is found that the proposed method can not only guarantee the objective quality score, but also significantly improve the perception effect. I think this paper can be published with a few modifications and adjustments.”

Response:

We really appreciate the reviewer’s positive comments and recognition of the contribution of the manuscript. We revise the manuscript based on the reviewers’ comments and believe the revised manuscript is substantially improved.

1.Abstract, P1 (answer to Reviewer #1, suggestion #1)

Comment:

“In the abstract part, the author does not explain the professional term "SISR". In order to improve the readability of the article, it is suggested to explain its meaning. In addition, a large part of the abstract is a description of the construction process of the SISR method, while the expression of the research results is only a short sentence. I think it is necessary to add more details about the results in combination with the research content.”

Response:

Thanks for pointing out our shortcomings! In this revision, we explain the professional term "SISR". Also we more concise expressions of the research results.

In the last manuscript:

In recent years, deep learning (DL) networks have been widely used in single image super-resolution (SR) (SISR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided SISR method is proposed in DL architecture…Evaluation on the benchmark datasets indicates that the proposed method can ensure the remarkable performance of both objective quality score and perception effect.

In the revised version:

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture... The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.

2.Introduction, P1 (answer to Reviewer #1, suggestion #2)

Comment:

“In the introduction, first, I have doubts about the term "LR". What does it mean? Then, I think the purpose and significance of the research should be highlighted in this part, which is of great significance to reveal the necessity of research work.”

Response:

Thanks for your suggestion! LR means low-resolution, which is explained in the first paragraph. According to your suggestion we add more explanation about our research purpose and significance in this revision.

In the revised version:

Despite the rapid development of imaging technology, imaging devices still have limited achievable resolution due to several theoretical and practical restrictions. Super-resolution (SR) technology provides a far promising computational imaging approach to generate high-resolution (HR) images via an existing low-resolution (LR) image or image sequences, which have been widely applied in video surveillance, medical diagnostic imaging, as well as radar imaging systems…

3.P5, Section 2.2, Paragraph 8 (answer to Reviewer #1, suggestion #3)

Comment:

“In Section 2.2 image quality evaluation, the author mentioned that among NR-IQA method and FR-IQA method, the method selected at last is NR-IQA method. However, there are a lot of descriptions about FR-IQA, which is unnecessary in my opinion. It is enough to emphasize the application advantages of NR-IQA method over FR-IQA method.”

Response:

Thanks for this suggestion! In this revision, we remove some descriptions about FR-IQA.

In the last manuscript:

FR-IQA methods refer to the quality assessment of distorted images by comparing with the original image, which is an undistorted version of the same image. The extent of distortion is calculated by measuring the deviation of distorted image from the reference image. The simplest approach to measure image quality is by calculating the PSNR and the structural similarity index metric (SSIM) [32]. However, PSNR and SSIM do not always correlate with human visual perception and image quality. Other IQA methods were proposed to address the limitation of PSNR and SSIM. IQA metrics, including visual information fidelity (VIF) [33], Fast SSIM [34], information fidelity criteria (IFC) [35], multi-scale structural similarity (MS-SSIM) [36], and mean deviation similarity index (MDSI) [37], correlate well with human perception. Although these methods have improved the accuracy of IQA, they are built on far more complex mathematical models and are mostly non-differentiable. This finding causes a great deal of inconvenience to solve the optimization problems. Therefore, PSNR and SSIM are still widely used by researchers.

In the revised version:

FR-IQA methods refer to the quality assessment of distorted images by comparing with the original image, which is an undistorted version of the same image. The simplest approach to measure image quality is by calculating the PSNR and the structural similarity index metric (SSIM) [32]. However, PSNR and SSIM do not always correlate with human visual perception and image quality. Other IQA methods were proposed to address the limitation of PSNR and SSIM. IQA metrics, including visual information fidelity (VIF) [33], Fast SSIM [34], information fidelity criteria (IFC) [35], multi-scale structural similarity (MS-SSIM) [36], and mean deviation similarity index (MDSI) [37], correlate well with human perception.

4.P6, Section 3, Paragraph 1 (answer to Reviewer #1, suggestion #4)

Comment:

“In the first paragraph of “Proposed methods”, it is mentioned that “In general, these networks have been trained interactively and, thus, promote the performance and robustness of SISR network.” I think the conclusion should be supported by some theories or references. Only a brief statement like this is unconvincing.”

Response:

Thank you for your suggestion. We go through the entire paragraph once again and adjust the organization of this paragraph to make it more clearly explained.

In the last manuscript:

These networks are creatively cascaded with each other in our work. Specifically, SR images produced by the SR network are imported to IQA network, which outputs the image quality scores. Meanwhile, this image quality indicator is regarded as feedback to SR structure, which guided the training of SR network. In general, these networks have been trained interactively and, thus, promote the performance and robustness of SISR network.

In the revised version:

These networks are creatively cascaded with each other in our work, in order to promote the performance and robustness of SISR network. Specifically, SR images produced by the SR network are imported to IQA network, which outputs the image quality scores. Meanwhile, this image quality indicator is regarded as feedback to SR structure, which guided the training of SR network.

5.P6, Section 3.1, Paragraph 2 (answer to Reviewer #1, suggestion #5)

Comment:

In Section 3.1, it is mentioned that “The leaky ReLu function [23] is added between the FC layers? Why choose the leaky ReLU and what are its advantages? Since this is part of the innovation contribution of the article, it is necessary to supplement the basis.

Response:

Thanks for your friendly reminder! The reason why we choose the leaky ReLU is explained in the following 6th line and we cite the relevant reference. (•‘To avoid gradient sparseness [26], Leaky ReLU is used to take the place of ReLU, and α is set to 0.2.’)

6.P6, Section 3.1, Paragraph 3 (answer to Reviewer #1, suggestion #6)

Comment:

“In Section 3.1, it is mentioned that "To save memory, the first and second down sampling are...". How to determine the original sampling coefficient? In order to highlight the feasibility of coefficient setting, it is necessary to briefly explain.”

Response:

Thank you for your suggestion. We add a briefly explanation about this sentence.

In the revised version:

• Since IQA network serves as providing feedback to guide parameters adjustment, the structure of IQA network should not be too complex. To save memory, the first and second down samplings are conducted on the factor of ×4 (the size of the corresponding convolution kernel is 5 × 5), and the others are achieved on the factor of ×2 (the size of the corresponding kernel is 3 × 3).

7.P6, Section 3.1, Paragraph 3 (answer to Reviewer #1, suggestion #7)

Comment:

“What is the meaning of "EDSR" below the Figure 2?”

Response:

EDSR is a famous SISR algorithm proposed by Lim B. et al. in ref [15]. It is explained in Section 2, Para 3(Lim [15] developed an enhanced deep SR network (EDSR)….)

8.P6, Section 3.1, Paragraph 3 (answer to Reviewer #1, suggestion #8)

Comment:

“In the next paragraph of equation 1, the author mentioned "the patches with the size of 16 × 16 are used for texture loss calculation". I have doubts about why the “patches” with the size of 16 × 16 are used.”

Response:

Images generated from MSE face the over-smooth problems, and thus, cannot match up to the human visual system. To solve this problem, Sajjadi proposed texture matching loss, which generated abundant texture information in ref [25]. The patches with the size of 16 × 16 is tested in ref [25]. Here we just keep the configuration in [25], hoping to accomplish the same effect in capturing the texture details.

9.P12, Section 4.2, Paragraph 6 (answer to Reviewer #1, suggestion #9)

Comment:

“In Section 4.2, PIRM self-validation was selected. There are no references or explanations in this part. Please add.”

Response:

Thanks for your careful reading. We add the corresponding reference in this sentence.

In the revised version:

• The testing experiments are designed on PIRM-self [53] validation set that provides smaller image size and faster computing speed…

10.P16, Section 5, Paragraph 3 (answer to Reviewer #1, suggestion #10)

Comment:

“In the conclusion part, it is very helpful to optimize the structure of the paper if the author can analyze and summarize the shortcomings of the research work, and put forward the planning and prospect of the future research work.”

Response:

Thanks for your suggestion. According to the problems you point out, we rewrite the conclusion to summarize the shortcomings of the research work and propose some future work.

In the revised version:

We add the following paragraph:

•Although the proposed method has achieved some good results, this work is just the beginning to implement this IQA-guided approach in SISR problem. In our work, the performance is proved through benchmark datasets, its robustness of different kinds of real data remains to be verified. In further works, we will develop this algorithm and make it applicable to more data types (such as infrared images, remote sensing images, et al.).

Response to Reviewer 2

General Comments:

“In order to achieve a good tradeoff between perceived quality and distortion measurement of service request results, and effectively improve image resolution, an image quality evaluation guided SISR method is proposed in service request architecture. From the results of stochastic resonance, IQA network is introduced to extract perceptual features. Through the cascade network, an interactive training model is established. The results show that the performance of the model is better, which can significantly improve the image resolution. However, the author needs to revise and clarify the following questions before the article is published.”

Response:

Thank you very much for your comprehensive comments and constructive suggestions. We read and consider each comment very carefully, and thoroughly revise the manuscript according to your comments and suggestions. We hope that the manuscript reads more convincingly after the revision.

1.Abstract, P1 (answer to Reviewer #2, suggestion #1)

Comment:

“In the abstract part, there is no description of the main results, conclusions and research significance of the paper, which makes it difficult for readers to see the contribution of the article to the field.”

Response:

Thanks for your suggestion. According to the problems you point out, we rewrite the abstract to highlight the contributions of our work.

In the last manuscript:

In recent years, deep learning (DL) networks have been widely used in single image super-resolution (SR) (SISR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided SISR method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. Evaluation on the benchmark datasets indicates that the proposed method can ensure the remarkable performance of both objective quality score and perception effect.

In the revised version:

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.

2.Introduction, P1 (answer to Reviewer #2, suggestion #2)

Comment:

“The author's scientific question is incorrect, not because SISR is practical in many aspects and it has high performance, it is necessary to study the algorithm. In this paper, the author should more elaborate on the practical significance of ultra-high resolution image and what is the difficulty of improving image resolution in reality?”

Response:

Thank you for your comment. We reorganize the logical structure of ‘Introduction’ according to your suggestion, and add more explanation about our research purpose and significance in this revision.

In the revised version:

Despite the rapid development of imaging technology, imaging devices still have limited achievable resolution due to several theoretical and practical restrictions. Super-resolution (SR) technology provides a far promising computational imaging approach to generate high-resolution (HR) images via an existing low-resolution (LR) image or image sequences, which have been widely applied in video surveillance, medical diagnostic imaging, as well as radar imaging systems….

SISR is an underdetermined inverse problem, and, thus, is relatively challenging because a given LR input image may have multiple solutions based on various texture details in its corresponding HR image….

3.Introduction (answer to Reviewer #2, suggestion #3)

Comment:

“Why do people need deep learning to improve image resolution? Why can't ordinary support vector method and linear regression method be applied to improve image quality.”

Response:

Conventional methods, including interpolation, frequency domain, support vector method and linear regression method approaches, can achieved high reconstruction efficiency in some cases. However, these methods exhibit limitations in predicting detailed, realistic textures. SR results from deep learning methods outperform those from other approaches in PSNR and perceptual evaluation given the inherent ability of SR to extract high-level features. For these reasons deep learning technique has gradually become the mainstream in SISR field.

4.Introduction, P1~P2 (answer to Reviewer #2, suggestion #4)

Comment:

“Please simplify the introduction and elaborate the main research background, research questions, research significance, previous research progress and innovation of this paper. Please reorganize the language according to this format, delete unnecessary background description, and highlight the research focus of the article.”

Response:

Thanks for pointing out our shortcomings! In this revision, we reduce some relatively unnecessary content of expressions of the basic theories. Also we replace some explanations of the research basis with shorter and more concise expressions to highlight the content related to the research progress. All changes are marked in our revised Manuscript.

5.Full text (answer to Reviewer #2, suggestion #5)

Comment:

“In this study, stochastic resonance network and IQA network are used to process images in series. However, in the related reports, the nonlocal residual neural network NR-Net and random forest QA method are used, and finally IQA is carried out on the image. This way of image resolution is also higher, and the accuracy of the model is better. Therefore, in this study, why use the combination of stochastic resonance network and IQA? According to the reference “Liu S, Thung K H, Lin W, et al. Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks[J]. IEEE Transactions on Image Processing, 2020.”

Response:

Thank you for your comment. According to your suggestion, we cite this reference paper in the ‘Related Work’ part in this revision. This work provides a good real-time quality assessment method of Pediatric MRI images. And it is not contradictory with our work: Firstly, we didn’t propose a stochastic resonance network, but a super-resolution (SR) network. Secondly, this the related report mainly introduces a reliable method IQA. However, our work focuses more on how to improve the resolution of a single image, and IQA functions as a feedback parameter.

6.P9, Section 3.3 (answer to Reviewer #2, suggestion #6)

Comment:

“What are the specific meanings of and G in equation 1 and m in equation 2? Please check the meaning of the equation letter and improve it.”

Response:

Thank you for your careful reading. G in equation 1 denotes the perceptual loss function of IQA net, and m in equation 2 denotes the margin used in the loss function, they are pointed out in the revision.

In the last manuscript:

where ISR is the SR image, IHR represents the reference HR image and wi(i = 0, 1, 2, ..., 5) is the weight coefficient of loss function.

In the revised version:

where ISR is the SR image, IHR represents the reference HR image, G denotes the perceptual loss function of IQA net, and wi (i=0, 1, 2, …, 5) is the weight coefficient of loss function.

7.P10-12, Section 4 (answer to Reviewer #2, suggestion #7)

Comment:

“When the model is trained, I suggest adding the proportion of data sets and tests used for deep learning to the total data. Details are added so that other readers can repeat the experiment.”

Response:

Thank you for your suggestion. Since the proportion of network parameters may need to be changed according to the types of training data. We just show some major parameters in this stage. This work is just the beginning to implement this IQA-guided approach in SISR problem, we will develop the details in the future work.

8.P11, Section 4.2, paragraph 1 (answer to Reviewer #2, suggestion #8)

Comment:

“In the model performance comparison and parameter determination, different iterations are key to these indicators. In this paper, in order to study the contribution of parameters k and n to the training effect, the iteration number of service request network is fixed at 60000, so as to ensure that all service request networks are trained to the same degree. However, the number of iterations is determined according to the previous experiments or according to the relevant references. Because the number of iterations is relatively large and there is no loss function result, I am not sure that your parameter is valid.”

Response:

Sorry for not providing more details. The iteration number was set to a very large number at 60000 to ensure that the potential of all model variants could be fully utilized, which is actually an empirical observation from our experiments.

9.P13, Section 4.2, paragraph 7 (answer to Reviewer #2, suggestion #9)

Comment:

“In part 4.2, it is mentioned that the weight coefficient of the loss component slightly affects the resonant frequency performance. However, the result is the method has good stability. Is there any inevitability between the two? Do you mean that the effect of Pi on the performance of RMSE is small, so the model is stable?”

Response:

Sorry for bringing you an ambiguity! Our original intention is to express that the performance is insensitive to the specific parameter (weight coefficients of the loss components), therefore brings great convenience in parameters adjustment. In the revision, we remove the ambiguous expressions, according to you suggestion.

In the last manuscript:

This result also shows that our method has good stability because it is insensitive to the weight coefficients, thereby rendering great convenience in adjusting the SISR parameters.

In the revised version:

This result also shows that our method is insensitive to the weight coefficients, thereby rendering great convenience in adjusting the SISR parameters.

10.P14, Table 3 (answer to Reviewer #2, suggestion #10)

Comment:

“I suggest that the contents of Table 3 should be represented by line chart, so that the differences of different methods in different data sets can be clearly seen, and the expression is more intuitive and clearer.”

Response:

Thanks for this suggestion! We’ve ever tried to show the results by line chart, since the values are too concentrated, it looks unclear and hard to compare the performance of different SR methods. To make the differences of different methods seen more clearly, we provide a scatter graph of RMSE versus PI for our methods and others (seen as Figure 6.), hoping to play a part of same role as the line chart form you have suggested.

11.Section 4 (answer to Reviewer #2, suggestion #11)

Comment:

“The image effect in this paper is obviously different from other algorithms, which proves the effectiveness of the algorithm. But why is it not compared with other deep learning algorithms, such as DCN and DDPG, which have relatively good performance in image sign extraction. Please explain.”

Response:

Thanks for this suggestion! We’d say that we have already compared our method with several deep learning-based methods in the experiments, which are EnhanceNet, EDSR, RCAN, EDSR-GAN, and EDSR-VGG2,2. These methods were published in recent years and achieved good performance in different datasets as reported by their papers, for which we think the comparison with them is convincing enough to show the effectiveness of our method. Compared with those methods above, image sign extraction methods like DCN and DDPG are not so irrelevant to the topic of this paper, so we do not include them.

12.P13, Section 4.4 (answer to Reviewer #2, suggestion #12)

Comment:

“In the result part of the paper, a large number of methods and references are added. I suggest that the two parts should be described separately, including training of data sets, and comparison of different algorithm models. These are part of the method, while the real results and the contents discussed need to be listed separately.”

Response:

Thank you for your suggestion! According to your suggestion, we separate the method descriptions with the results part.

13.P16, Section 5 (answer to Reviewer #2, suggestion #13)

Comment:

“In the conclusion part, more content is to elaborate the significance of this study, but the main research results are not summarized. For example, what are the advantages of the model mentioned in this paper compared with other models? How much is the image resolution improved? In addition, the author is required to add the advantages and disadvantages of this study and put forward the future research direction.”

Response:

Thanks for your suggestion. According to the problems you point out, we rewrite the conclusion part to explain the advantages of the proposed models and summarize the shortcomings of the research work and propose some future work.

Response to Reviewer 3

General Comments:

“In this study, a deep learning architecture based single image super-resolution method guided by image quality assessment is proposed to achieve a good compromise between perceived quality and distortion measurement of super-resolution results. However, the method proposed in this paper is different from the super-resolution method of opportunity deep learning. In this paper, an image quality assessment network is introduced to extract perceptual features from super-resolution. By calculating the corresponding loss fused with the original absolute pixel loss, the adjustment of super-resolution network parameters is further guided. At the same time, through the establishment of interactive training network model, a hinge loss method based on acceptance sorting is proposed to overcome the shortcomings in the training process and solve the problem of image quality assessment and heterogeneous data sets used in super-resolution network. However, some contents of this paper need to be modified to meet the requirements of journals. The author is requested to revise it according to the following contents. I hope to see the revised content in the next manuscript.”

Response:

We really appreciate the reviewer’s positive comments and recognition of the contribution of the manuscript. We revised the manuscript again based on the reviewers’ comments and believe that the revised manuscript is substantially improved.

1.Abstract, P1 (answer to Reviewer #3, suggestion #1)

Comment:

“At present, the content of the abstract is a little confused, which needs the author to elaborate according to the research purpose, research methods, research results and conclusion mode, so as to highlight the research content of this paper, and then let readers have a clearer understanding of the content of the article.”

Response:

Thanks for your suggestion. According to the problems you point out, we rewrite the abstract to highlight the research content of this paper.

In the last manuscript:

In recent years, deep learning (DL) networks have been widely used in single image super-resolution (SR) (SISR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided SISR method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. Evaluation on the benchmark datasets indicates that the proposed method can ensure the remarkable performance of both objective quality score and perception effect.

In the revised version:

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.

2.Introduction, P1~P2 (answer to Reviewer #3, suggestion #2)

Comment:

“In the introduction, the fourth paragraph does not conform to the research background. There should not be a large section of the research results here. Moreover, the first three paragraphs for the background of the study is not in-depth, so I cannot understand the content of this paper. It is suggested that the author reinterpret this part.”

Response:

Thanks for pointing out our shortcomings! In this revision, we reorganize the logical structure of ‘Introduction’ according to your suggestion, and add more explanation about our research purpose and significance in this revision. All changes are marked in our revised Manuscript.

3.P3, Introduction (answer to Reviewer #3, suggestion #3)

Comment:

“The last part of the introduction is not suitable to be elaborated in the introduction. It is suggested that the contribution of this paper should be put into the conclusion, and the research significance and innovation of this paper should be elaborated at the end of this part.”

Response:

Thanks for your suggestion! We summarize the contribution in the conclusion. Yet, after a fully discussion, we think it is also necessary to list our contributions here to help the readers better understand what we have done before they read the conclusion.

4.P4, Section 2 (answer to Reviewer #3, suggestion #4)

Comment:

“In the related work, more literature review content has been added, which is repetitive with the content described in the introduction. It makes me feel that the author is quoting the results of others and has no own research content. The author has better adjust these contents. Previous scholars' research is only used for reference, not to complete your article by expounding the previous scholars' research content and research results. Otherwise, the research article is more like a summary of the study.”

Response:

We'd like to thank you for pointing out this problem. We adjust this part and reduce some relatively unnecessary content of expressions of the basic theories. Also we replace some explanations of the research basis with shorter and more concise expressions to highlight the content related to the research progress.

5.P6, Section 3.1, paragraph 1(answer to Reviewer #3, suggestion #5)

Comment:

“In this paper, image quality assessment based on deep learning is mentioned, but I don't see how to apply deep learning to image quality assessment network. In this paper, the author only describes "such as CNNIQA [44], extracting sufficient feature information is difficult. Relatively deep architectures, such as Hallucinated-IQA". Which method of deep learning is used to extract features? If a convolutional neural network is used, it should be directly described, rather than expressed in such a way as this.”

Response:

Thanks for your kind suggestion. We explain about which IQA model is used in the next paragraph “As shown in Figure. 2, an improved DeepIQA (DeepIQA has two versions: FR version and R version, we apply NR version in this work) is used as the IQA net in our work. The figure shows that the feature extraction is realized by cascading convolutional layers in five levels, with increasing channel numbers of 32, 64, 128, 256, and 512…”

We mention CNNIQA [44] and Hallucinated-IQA here only to demonstrate that not every IQA model is suitable in our SISR problem.

6.P7, Section 3.2 (answer to Reviewer #3, suggestion #6)

Comment:

“When the network is trained, the author does not explain the origin of the data set, and the author needs to supplement this part.”

Response:

Thanks for your kind suggestion. We explain the origin of the data set in paragraph Section 4 ( ‘To evaluate the performance of the proposed method, the networks were trained on the training set of DIV2K [52].’). Since Section 3.2 is written only to explain the training process in our work, we haven’t yet explained the specific dataset used in training here.

7.P7, Section 3.2 (answer to Reviewer #3, suggestion #7)

Comment:

“The content of Section 3.2 is about training, but this part is about the content of the method. It is suggested to put it into the experiment of the fourth part.”

Response:

Thanks for your kind suggestion. Section 3.2 actually explains the training strategy used in our paper, detailed training parameters is introduced in Section 4 (the experiment part). In order to avoid bringing about ambiguity, we revise the title of this section.

8.P11, Section 4.1 (answer to Reviewer #3, suggestion #8)

Comment:

“The fourth chapter belongs to the content of analysis, that is, the results and discussion. It is not appropriate to continue to elaborate the equation in this part, such as equation (8). It is suggested to put this part in the method section. In the fourth chapter, it only necessary to describe the research results and discussion content.”

Response:

Thanks for your reminder. We move this part to the Section 3 ahead, according to your suggestion.

9.P11, Section 4.1 (answer to Reviewer #3, suggestion #8)

Comment:

“Below Figure 4, it is mentioned that ‘Although the result from the 2nd set of parameters executes a better PI performance than the first two sets, much longer time and more iterations (120, 000) are consumed for training than the 4th set of experiments (100, 000 iterations).’. However, I don't see the results under different iterations in this paper, so the author needs to supplement.”

Response:

Thanks for your reminder. The paragraph which you point out is an explanation of a small experiment about the process of parameter adjustment. Indeed, it is more perfect to experiment with different iterations according to your suggestion. However, it will take a plenty of time to compare different iterations using different datasets and it is unable to complete in this phase. This work is just the beginning to implement this IQA-guided approach in SISR problem, and we really appreciate your valuable advice. We would like to accomplish the advice your have proposed in the future work.

10.P13, Section 4.2 (answer to Reviewer #3, suggestion #10)

Comment:

“What is the meaning of Figure 5? The result shown in Figure 5 at present is beyond my comprehension. The author needs to consider adjusting the coordinates so that the contents in the diagram can be displayed as much as possible, or expressing it in a different way.”

Response:

Figure 5 shows the changing trends about the value of PI and RMSE. From Figure 5, it is clear that the points tend to gather. It is placed here to demonstrate that the performance of the model in terms of PI and RMSE changes a little with varying weight coefficients of loss components, which implies that our model is insensitive to the weight coefficients, thereby rendering great convenience in adjusting the SISR parameters.

11.P8, Section 3.3 (answer to Reviewer #3, suggestion #11)

Comment:

“In the method, the loss function is mentioned, but the comparison of loss function is not given in the paper. The author needs to adjust the content or expression.”

Response:

Thanks for your reminder. Actually we compare and analyze the different kinds of loss functions in the ‘Introduction’ and ‘Related Work’ part (see. “According to the loss function, existing deep learningbased SR methods fall largely into two categories: 1) Absolute loss (AL)-based methods [10–13]. AL-based methods mainly focus on improving the quantitative indicator of IQA and generally take the forms of MSE or MAE. Results show that this method can achieve favorable results with high peak signal-to-noise ratio (PSNR) value provided that the…”). In order to avoid repetition, we just show the process of how we design the loss function without displaying the comparison of loss function here.

12.Full text (answer to Reviewer #3, suggestion #12)

Comment:

“The tables in this paper can be converted into figures as much as possible, so that the results can be observed more intuitively.”

Response:

Thanks for your suggestion. Since PLOS ONE require for editable original data, we design our tables like this. According to your suggestion, we prepare a copy of tables with figure forms, and we would contact with the editor to ensure a both intuitive and acceptable form.

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