Anomaly-focused Single Image Super-resolution with Artifact Removal for Chest X-rays using Distribution-aware Diffusion Model

Authors: dattatreyo  roy
angshuman  paul
roy  paul
Venue: MIDL_io_2024_Conference
Published: 2024-01-25 15:10:53

Abstract: Single image super-resolution (SISR) is a crucial task in the field of medical imaging. It transforms low-resolution images into high-resolution counterparts. Performing SISR on chest x-ray images enhances image quality, aiding better diagnosis. However, artifacts may be present in the images. We propose an anomaly-guided SISR process utilizing the denoising mechanism of the diffusion model to iteratively remove noise and restore the original image. We train the model to learn the data distribution, enabling it to eliminate artifacts within the images. Additionally, we ensure reconstruction of the disease regions by prioritizing their reconstruction. Our research experiment over the publicly available dataset and find that the existing SISR methods are unable to learn and remove these artificially added artifacts. On the other hand, our proposed model not only prioritizes superior image reconstruction but also remove the artifacts. Our method is found to outperform the existing methods. The code is publicly available at https://github.com/Datta-IITJ/MIDL_code.git.

Main paper: 
 introduction: 
 Medical images with superior resolution may provide important information about various abnormalities that may be present in such images. Such information is likely to play a crucial role in the diagnosis. Chest x-ray is one of the most widely used imaging modalities. Chest x-rays with a superior resolution may facilitate the diagnosis of various abnormalities by radiologists. Furthermore, in various applications including telemedicine, it may be required to compress the size of such images. However, at the time of diagnosis, the original resolution of those images should be restored. Therefore, improving the resolution of chest x-ray images may potentially aid various aspects of modern healthcare.Single image super-resolution (SISR) methods aim to create high resolution (HR) images from their low resolution (LR) counterparts. In spite of the significant progress in the field of SISR (Ledig et al., 2017; Wang et al., 2018; Saharia et al., 2022; Li et al., 2022) , such methods are relatively rare for medical images including chest x-rays. In (Yu et al., 2021) , the authors presented WFSAN, a lightweight architecture for high-quality medical image super-resolution. SNSR-GAN was proposed in (Xu et al., 2020) for enhancing chest x-ray images. The authors of (Monday et al., 2022) introduced the COVID-SRWCNN which employs a siamese wavelet multi-resolution convolutional neural network. For anomalydriven SISR of chest x-ray images, see (Yadagiri et al., 2023) .• The proposed model utilizes information about abnormalities that may be present in the chest x-rays. The resultant SR image is likely to contain richer information about the abnormalities.• Our model captures the distribution of data using a variational autoencoding mechanism to facilitate super-resolution.• Experiments on publicly available datasets show the potential of generalizability of the proposed method.The rest of the paper is organized as per sections. Section 2 consists of the methodology where we mention details regarding our proposed method and the training process. Section 3 is the experimental details and the quantitative and qualitative results obtained using our methodology. Section 4 contains the conclusion. 
methods: 
 We design a method for the single image super-resolution of chest x-rays. To design this method, we consider the following facts. Due to various reasons, artifacts may be present in LR chest x-ray images. Removal of these artifacts during super-resolution may facilitate the diagnosis. Diffusion models can iteratively remove noise from images. Therefore, diffusion models may be helpful in removing artifacts. So, we use a diffusion model-based approach for SISR. We also note that in the super-resolved images, if we have rich information about the abnormality present in the x-ray images, diagnosis may be improved. Therefore, we design an anomaly-guided SISR method. Furthermore, we also consider that capturing the distribution of the data during the super-resolution process may help in achieving a superior SR image. Thus, as a backbone of our model, we utilize a U-net based architecture (Ronneberger et al., 2015) that explicitly capture the distribution of the data. A block diagram of this architecture is presented in Fig. 1 .We build our super-resolution model on the design of (Saharia et al., 2022) . During training, we employ a forward Markovian diffusion process to gradually add Gaussian noise to a highresolution (HR) chest x-ray image over T iterations. In the reverse process, we iteratively denoise the above noisy image to get back the HR image through T iterations following (Saharia et al., 2022) . At each iteration of the reverse process, the model is expected to generate a less noisy image Y T -1 from a more noisy image Y T obtained from the previous iteration. The eventual output (after all the iterations of the reverse process) of the model is the Super-resolved image. During the inference process, we take an LR chest x-ray image and perform bicubic interpolation to improve the resolution. The bicubic interpolation only provides an approximate super-resolved image. After adding noise, this interpolated image becomes noisy. Subsequently, the reverse process is applied to this noisy image to generate a super-resolution (SR) image from it. The method of (Saharia et al., 2022) is designed using a U-net backbone to perform the reverse process. We modify the design of (Saharia et al., 2022) in such a way that we capture the distribution of the data during the process of super-resolution. We also emphasize the abnormal regions during this process.To capture the distribution of the data through our U-net backbone, we employ a variational autoencoding mechanism. Variational autoencoders (VAE) (Kingma and Welling, 2013) can capture the distribution of data. We modify the structure of the bottleneck layer of our U-net backbone to act like the latent layer of VAE. In this context, we note that U-net consists of an encoder-decoder architecture. Therefore, if we can enforce a distributional similarity in the bottleneck layer of U-net, the modified U-net can emulate the properties of VAE in the context of capturing data distribution. Therefore, in our method, the latent layer of the VAE captures the distribution of the input data after the data is transformed by the encoder part of U-Net.To that end, we add a few layers parallel to the bottleneck layer of the U-net (see Fig. 1 ). We abbreviate the original bottleneck layer of U-net as BLU and the newly added layers as BLVE, BLVL and BLVD. Layers BLVE and BLVD act like an encoder and a decoder layer, respectively. Layer BLVL acts like the latent layer of a VAE. Let Z be the representation of transformed input data at BLVL. We calculate the KL divergence loss (L KL ) between Z and a standard normal reference distribution. Minimization of this makes the distribution of Z similar to the reference distribution. Loss L KL helps in capturing the distribution of this data in latent layer BLVL. Since the output of BLVD is generated by sampling from the distribution learnt in BLVL, the output quality from BLVL and thereby the output of the proposed method is likely to be dependent on the distribution of the data captured in BLVL. To enforce autoencoding, we also calculate a reconstruction loss (L V R ) between the input to BLVE and the output from BLVD.A better reconstruction of the regions with abnormality may aid the diagnosis. So, we aim to provide additional emphasis on the reconstruction of such regions. To that end, we utilize the bounding box (BB) annotations of the abnormalities in the chest x-ray images. We calculate the mean-squared error of the pixel intensities inside BB between the SR and the corresponding HR images. This error serves as bounding box loss (L B ). We ignore the BB-loss component if an image does not have the BB information (e.g., images with no anomaly).In addition, we also compute a reconstruction loss (L R ) between the original HR image without artifacts and the SR image generated by our method. Minimization of this loss helps in making the SR image similar to the HR image. During the training of the proposed method, we minimize the following loss:EQUATIONwhere λ R , λ B , and λ VAE represent the weights governing the relative importance of each component. L R and L B are already mentioned above. L VR is the reconstruction loss between the representations of the input data created by layers BLVE and BLVD. L KL is the KL divergence loss between the representation Z at BLVL and a standard normal reference distribution.A major goal of the proposed method is to remove artifacts during super-resolution. For that purpose, we propose the following training protocol. First, we add artifacts in the LR images. The original chest x-ray artifacts have circular shapes, letters, digits, and lines (art; bra). We tried to create similar artifacts. During the training, we use these images with artifacts as input and try to generate super-resolved images without artifacts. Our diffusion model, being suitable for removing noise is expected to treat the artifacts as noise and learn to remove those artifacts during the process of super-resolution.During inference, we apply an LR chest x-ray image with artifacts to our trained model. First, a bicubic interpolation is performed to create an approximated super-resolved image.Noise is added to this image. We continue adding the noise until we create a fully noisy image up to T time-steps. Subsequently, the SR image is generated from the interpolated noisy image through the reverse process. A step-wise visualization of the diffusion process is presented in Appendix A.The backbone of the proposed model is a U-net structure similar to the one used in (Saharia et al., 2022) . However, from the down-sampled stage, we create a branch for implementing variational autoencoding. This branch has got one encoding convolutional layer BLVE, one decoding convolutional layer BLVD, and a fully connected latent layer BLVL. We then concatenate the reconstructed outputs from the bottleneck layer of the U-net and BLVD layer. It then goes to the up-sampling stage where it reconstructs the image back to the required dimension.To add noise to the images, we use a linear schedule adopted over 1000 time-steps denoted as T in Fig. 1 . The noise level initiates at 1 × 10 -4 and gradually increases to 1 × 10 -2 . Our model is trained using the Adam optimizer with a learning rate of 1e-4 and batch size of 2. We include the hyperparameter details of the competitive methods in the Appendix B. For the calculation of loss, we use λ R as 0.4, λ B as 0.3, and λ V AE as 0.3. All these parameters are selected based on the validation performance. 
datasets: 
 We use two publicly available chest x-ray datasets for our experiments. These are VinBig chest x-ray dataset (Nguyen et al., 2022) and NIH Chest x-ray14 dataset (Wang et al. , the VinBig dataset. The NIH dataset comprises 112,120 x-ray images with disease labels from 30,805 patients. We use 3000 images from the NIH dataset for testing only.We compare the performance of the proposed method with several state-of-the-art SISR techniques including SRCNN (Dong et al., 2015) , SRGAN (Ledig et al., 2017) , ESR-GAN (Wang et al., 2018) and SR3 (Saharia et al., 2022) . The details of hyperparameters for the competing methods are presented in Appendix C. The performances are evaluated based on Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculated using SR image produced by a method and the ground truth HR image (without artifacts). All the models are trained on the VinBig training dataset and tested on the Vin-Big test dataset and the NIH test dataset. We train the proposed method and all SOTA methods using the same training images with same artifacts. In all the experiments, an LR input image of size 64×64 is created from the original HR image by down-sampling. Subsequently, artifacts are added to these LR images. We train the various models to create super-resolved images of size 256×256 from these LR images. Thus, the numerical and visual results not only show the efficacy of the proposed method for super-resolution but also show its effectiveness in removing artifacts while performing super-resolution.The results using the different methods for ten runs are reported in Table 1 in terms of PSNR and SSIM. Notice that the proposed method outperforms all its competitors on both the datasets. Results on sample images using different methods are presented in Fig. 2 . Additional results showing the various images with and without artifacts and the output of the proposed method are presented in Fig. 4 of Appendix C. A statistical analysis between the performance of SR3 and the Proposed method is presented in Appendix E.Since we train the SISR models with the VinBig dataset, the results on the NIH dataset is an indicator of the generalizability of the SISR methods. The abnormalities present in 1 , notice that the proposed method outperforms all the competing approaches in this context. Thus, we conclude that the test results on the NIH dataset show the ability of our model to generalize to unseen anomalies as well.We perform various ablation studies to look into the importance of different components of the proposed method. All the ablation studies are performed on the VinBig dataset. First, we evaluate the importance of the bounding box loss of (1). To that end, we train our model excluding the bounding box loss (abbreviated as W-BBLoss). We also look into the impact of capturing the data distribution in our model. For this purpose, we train our model without the variational autoencoding branch (abbreviated as W-DataDist). The results of these ablation studies are presented in Table 2 . Notice that for both of the ablation studies, we obtain inferior results compared to the proposed method. These results signify the importance of different components of the proposed method. 
conclusion: 
 We introduce a SISR method for chest x-rays that eliminates artifacts during super-resolution.Our method employs diffusion model to facilitate an iterative denoising. A novel bounding box helps to emphasize the abnormal regions and produce richer information about the abnormalities in the SR images. We design a variational autoencoding mechanism in our architecture to capture the underlying data distribution during super-resolution. A novel training strategy helps in removing the artifacts. Rigorous experiments show not only the usefulness of our method in publicly available datasets but also its generalizability. Ablation studies show the impact of different components in our design. In the future, we will explore the possibility of using similar methods in other radiology images including CT. We will also look into the possibility of utilizing auxiliary information for SISR.  
 
 Appendix:  


Decision: The reviewers all appreciate the exhaustive experiments to assess the effect of the VAE that the authors added to the standard U-net (including generalization experiments that the authors ran around the clock for the rebuttal). While concerns remain about the theoretical justification of the VAE and the clinical applicability of method potentially suffering from hallucination, this is still a methodological twist that may be of interest to the MIDL community. I recommend borderline acceptance for this paper.

Reviews: review: paper_summary: paper_summary: The authors propose an approach to address the image quality enhancement problem in Chest X-rays. In their approach, the authors build upon SR3, a diffusion model used for image super-resolution, and incorporate additional components such as bounding box and VAE loss for artifact removal. Experimental results indicate that their approach outperforms SR3 and other baseline methods in terms of performance.
main_review: main_review: Despite the authors demonstrating the significance of their method over the SR3 baseline, I remain unconvinced about how the adoption of the latent VAE aids the network in restoring the image mathematically. Furthermore, I observed that some of the visualizations are not aligned (see the third column of Figure 4), raising concerns about whether this inconsistency might impact the quality metrics. Also, upon closer examination of the visualizations, it appears that the artifacts may not completely vanish from the image but instead manifest as body parts. This raises doubts about the clinical applicability of this work, which has not been addressed in the limitations section.
strength_weakness: strength_weakness: 1. The mathematical foundations concerning the diffusion probabilistic models for this problem setting are missing, making the approach difficult to understand.
2. Hallucinations render this approach challenging to utilize in a clinical environment.
3. The writing style of the paper can be enhanced for better clarity and readability.
questions: questions: Mentioned in the detailed comments section.
paper_summary: paper_summary: This paper proposes using an image denoising diffusion model to perform image super-resolution and remove artifacts at the same time.

The network architecture is a U-net with an additional VAE architecture in the latent space.

The model is trained with pairs of (artifact-added low-resolution image, clean high resolution image).
main_review: main_review: Thank you to the authors for answering some of the questions. The clarification for the artifact generation is helpful.
I maintain my original rating of weak accept. The rationale behind the variational approach still seems a bit hand-wavy, especially since it's not immediately obvious or explained very clearly why the variational formulation of the latent variables should lead to improved performance. The rebuttal argument about KL is not very convincing since that's simply a part of the VAE formulation.
strength_weakness: strength_weakness: It's unclear how close the generated artifacts are to real X-ray artifacts (unfilled rectangles and circles in the high resolution image and then downsampled).

The motivation behind adding a VAE bottleneck layer is unclear. From the description, it sounds like $\mathcal{L}_{VR}$ is the reconstruction loss between the input to BLVE and output from BLVD. This means the autoencoding is being performed with respect to the latent variables -- why does this help boost performance for the original super resolution / artifact removal task? Is it possible that you just needed to increase the number of channels to the BLU to achieve similar performance? The claim that the VAE branch was "capturing the data distribution", and that's the reason why there's some improvement is currently not very convincing.
questions: questions: Addressing the two main comments in the "weaknesses" would be helpful.
paper_summary: paper_summary: This paper proposes a modified diffusion model for Chest X-ray super-resolution, incorporating artifact augmentation, a variational architecture as the diffusion backbone, and an anomaly-focused loss. Both an ablation study and in-domain and out-of-domain test comparisons were provided, demonstrating better performance of the proposed method compared to other method.
main_review: main_review: I thank the authors for their response and additional experiments on generalization test during this short rebuttal period. I'm satisfied with the new results/descriptions and would like to increase my rating to '4: weak acceptance'.
strength_weakness: strength_weakness: - U-Net architecture: In Figure 1, it appears that the U-Net architecture lacks skip connections from the encoder to the decoder. If skip connections were not used, the architecture should be referred to as an autoencoder instead of a U-Net.
- The empirical analysis that VAE captures data distribution is misleading. The authors used VAE on the bottleneck features instead of on the original image data, so the VAE used here should work as a regularization on the latent space feature, not necessarily capturing the `data distribution’.
- Training other SOTA methods: Did you include anomaly augmentation and bounding box loss when training other SOTA methods too?
- Generalization to unseen anomaly: It is not clear whether the trained model can generalize to unseen anomaly.
questions: questions: Please respond to the comments regarding skip connections and training other SOTA methods. Please also consider modifying the empirical analysis of using latent space VAE. Future work should include testing generalization to unseen artifacts.

Comments: title: Addressing the questions to be addressed in Rebuttal.
Addressing the comments mentioned in the "weaknesses" section.
Addressing the comments mentioned in the "weaknesses" and "Detailed Comments" section
comment: $\textbf{1. Comment:}$ In Figure 2, I have noticed that some changes occur in the images after using your approach, especially in the leftmost image. Have you investigated how these hallucinations affect the performance of your model from a medical point of view? Despite ESRGAN and SRGAN's inability to remove artifacts and their low performance, at least they do not alter the location and shape information of the bones. I do not think it is safe to use diffusion models for medical image super-resolution and artifact correction at the moment unless you have an approach to mitigate this issue. You may want to look at other diffusion-based methods such as I2SB which is suitable also for your problem setting.

$\textbf{Response:}$ In this work, we have not investigated the quality of the output images produced by our model from a medical point of view. However, the performance measures like PSNR and SSIM used in our paper focuses on the quality of the output image based on its resemblance with the ground truth (GT) image. While PSNR focuses on the similarity between the intensities of the output image and the GT image, SSIM focuses on structural similarities between them. Our method outperforms the competing approaches (as evident from Table 1) in most occasions. This shows the ability of our method in producing output images that are similar to the GT images.  In the future, we will evaluate the quality of our output images from a medical view point. We will also look into the I2SB method. We thank the reviewer for the suggestions.


$\textbf{2. Comment:}$ How do you perform the sampling of your probabilistic model? Given that your method is based on SR3, I assume it is a modified version of DDPM. Nevertheless, I see that you have changed some hyperparameters like T decreasing it from 2000 to 1000. Could you describe your motivation behind such changes? 

$\textbf{Response:}$ Through validation performance, we find that using 1000 as the timesteps yields superior results compared to using 2000 as timesteps. Thus, based on the validation performance, we decide the value of the timesteps (a hyperparameter of our model). This is already mentioned in Section 2.6. 

$\textbf{3. Comment:}$ The shift in image intensity distribution in SRGAN is noticeable. Are there any reasons why?

$\textbf{Response:}$ The presence of the artifacts may change the intensity distributions of the input images. The SRGAN model may have more difficulty in dealing with such shifts in the distribution compared to other competing models including ours. The shift in the image intensity distribution in the output images by the SRGAN model may be the result of this difficulty that more severely affects the performance of the SRGAN model.

$\textbf{5. Comment:}$ The purpose of the green and blue arrows in Figure 1 is unclear. Does the operation depicted in Figure 1 demonstrate how you modify an existing Residual block, or do you construct an architecture that works in a parallel fashion to U-Net?


$\textbf{Response:}$ We use the SR3 baseline with additional modifications. We add the BLVE, BLVL and BLVD layers parallel to the BLU which is the bottleneck part of the U-net model. The above layers just work parallel to the bottleneck layer(BLU) of the U-net model. The Green and Blue arrows indicate only the down-sampling and up-sampling operation. We have mentioned this in the caption of Figure 1 in the revised manuscript.

$\textbf{6. Comment:}$ Like VAEs, diffusion models are also capable of modeling the data distribution,p(x). However, given the minimal improvement over the baseline method SR3, I am unsure whether it brings more merit considering the marginal computational load. Have you considered performing a statistical significance test on the methodologies?

$\textbf{Response:}$ To statistically evaluate the performance of our approach, we perform a statistical significance test. For each method, we perform ten rounds of experiments. At each round, we evaluate PSNR and SSIM on the VinBig and NIH dataset through. Using those values of PSNR and SSIM values for both the VinBig and NIH test data, we perform a t-test to look into the statistical significance of the difference in performance between our method and SR3. The p-values of these experiments are reported in Figure 5 of Appendix E. For NIH, the p-values for PSNR and SSIM are 0.0029 and 0.0018, respectively. For VinBig, the p-values for PSNR and SSIM are 0.0011 and 0.0158, respectively. From those results, we prove that the improvement by the proposed method is meaningful and not just by chance. Notice that all the p values are significantly less than 0.05. Therefore, we can conclude that the results of our method are statistically significantly different from those of SR3.


Additional changes: We will position the HR image at the bottom in Figure 2, add the mathematical descriptions of the loss terms in Section 2.3, and proof-read for the mentioned misuse of lower and upper cases.
$\textbf{1. Comments of the reviewer:}$ U-Net architecture: In Figure 1, it appears that the U-Net architecture lacks skip connections from the encoder to the decoder. If skip connections were not used, the architecture should be referred to as an autoencoder instead of a U-Net.

$\textbf{Author's response:}$
We are sorry for the confusion. Our design is motivated from the SR3 model [1] which uses U-Net as backbone. We also use U-Net architecture having skip connections. We have now modified Fig. 1 to show the skip connections.

$\textbf{2. Comments of the reviewer:}$
The empirical analysis that VAE captures data distribution is misleading. The authors used VAE on the bottleneck features instead of on the original image data, so the VAE used here should work as a regularization on the latent space feature, not necessarily capturing the `data distribution’.

$\textbf{Author's response:}$ We agree that we use the VAE on the bottleneck features. However, the bottleneck features are generated through transformation of the input data by the neural network layers of the encoder of U-Net.  This is very similar to what happens in a vanilla VAE [2]. Therefore, in our method, the latent layer of the VAE captures the distribution of the input data after the data is transformed by the encoder part of U-Net. By ‘data distribution’, we mean the distribution of this transformed input. We will add this detail in Section 2.2 of the revised manuscript. We thank the reviewer for the comment.  

 
$\textbf{3.Comments of the reviewer:}$ Training other SOTA methods: Did you include anomaly augmentation and bounding box loss when training other SOTA methods too?

$\textbf{Author's response:}$ We have trained the proposed method and all SOTA methods using the same dataset (same set of training images) with the same anomaly augmentations.  We revise section 3.2 to add these details.

On the other hand, the use of bounding box loss is a contribution of the proposed method and the vanilla SOTA methods do not include such a loss. Therefore, while doing the comparisons, we do not include bounding box loss in SOTA methods. 

$\textbf{4.Comments of the reviewer:}$ Generalization to unseen anomaly: It is not clear whether the trained model can generalize to unseen anomaly.

$\textbf{Author's response:}$ We thank the reviewer for pointing this out. In order to look into the generalizability of the proposed method, we perform experiments by training our model with the VinBig dataset and testing the trained model on the NIH dataset. Note that there are some abnormalities which are not present in the VinBig dataset but are present in the NIH dataset (e.g, edema, hernia). Therefore, when we perform testing on the NIH dataset (with the model trained in VinBig dataset), we encounter some unseen anomalies (such as edema, hernia) in the test data. Thus, the test result on the NIH dataset shows the ability of different methods (see Table 1) including our method to generalize to unseen anomalies as well. From Table 1, notice that our method outperforms all the competing approaches in this context. We revise section 3.3 to include this discussion.

In our future work, we will test generalization on unseen artifacts. We thank the reviewer for the suggestion.

$\textbf{Reference:}$
1. https://arxiv.org/abs/2104.07636

2. https://arxiv.org/abs/1312.6114
$\textbf{1. Comments of the reviewer:}$
It's unclear how close the generated artifacts are to real X-ray artifacts (unfilled rectangles and circles in the high resolution image and then downsampled).

$\textbf{Author's response:}$ The original chest x-ray artifacts comprise circular shapes, various letters and digits, and lines of different types [1, 2]. We have tried to generate similar types of artifacts for our experiments. For example, the circles we have generated resemble the circular artifacts. Similarly, part of our rectangular artifacts may emulate artifacts of the shape of a line.  Furthermore, we also notice that our model can remove artifacts consisting of letters and digits which were present in the original x-ray images. We revise Section 2.4 to include this discussion. 

$\textbf{2. Comments of the reviewer:}$ The motivation behind adding a VAE bottleneck layer is unclear. From the description, it sounds like $L_{\text{VR}}$ is the reconstruction loss between the input to BLVE and output from BLVD. This means the autoencoding is being performed with respect to the latent variables -- why does this help boost performance for the original super resolution / artifact removal task? Is it possible that you just needed to increase the number of channels to the BLU to achieve similar performance? The claim that the VAE branch was "capturing the data distribution", and that's the reason why there's some improvement, is currently not very convincing.

$\textbf{Author's response:}$ We introduce a VAE mechanism in our design. We agree that $L_{\text{VR}}$ captures the reconstruction loss. However, the loss component $L_{\text{KL}}$ helps in capturing the distribution of the data in the latent layer BLVL. The output of BLVD is generated by sampling from the distribution learnt in BLVL. Therefore, the quality of the output from BLVL and thereby the output of the proposed method is likely to be dependent on the data distribution captured in BLVL. We revise the second paragraph of Section 2.2 to include this discussion.


$\textbf{3. Comments of the reviewer:}$ “The resultant SR image is likely contain richer information about the abnormalities.” - grammer

$\textbf{Author's response:}$ We thank the reviewer for pointing out this mistake. We will rectify it in the revised version in the relevant place.

$\textbf{4. Comments of the reviewer:}$ “Auto-encoding variational bayes" - 2022? Seems like a mistake.

$\textbf{Author's response:}$ We thank the reviewer for pointing out this mistake. It will be rectified in the revised version.

$\textbf{Reference:}$
1. https://www.radiologymasterclass.co.uk/gallery/chest/quality/chest-x-ray-artifact
2. https://radiopaedia.org/cases/brassiere-artifact