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Shenzhen is a modern metropolis, but it hides a variety of valuable cultural heritage, such as ancient murals. How to effectively preserve and repair the murals is a worthy of discussion question. Here, we propose a generation-discriminator network model based on artificial intelligence algorithms to perform digital image restoration of ancient damaged murals. In adversarial learning, this study optimizes the discriminative network model. First, the real mural images and damaged images are spliced together as input to the discriminator network. The network uses a 5-layer encoder unit to down-sample the 1024 x 1024 x 3 image to 32 x 32 x 256. Then, we connect a layer of ZeroPadding2D to expand the image to 34 x 34 x 256, and pass the Conv2D layer, down-sample to 31 x 31 x 256, perform batch normalization, and repeat the above steps to get a 30 x 30 x 1 matrix. Finally, this part of the loss is emphasized in the loss function as needed to improve the texture detail information of the image generated by the Generator. The experimental results show that compared with the traditional algorithm, the PSNR value of the algorithm proposed in this paper can be increased by 5.86 db at most. The SSIM value increased by 0.13. Judging from subjective vision. The proposed algorithm can effectively repair damaged murals with dot-like damage and complex texture structures. The algorithm we proposed may be helpful for the digital restoration of ancient murals, and may also provide reference for mural restoration workers.
Keywords
Mural restoration, Mural in Bao'an District, Generator network, Discriminator network.
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CURRENT STATUS: Published
View the published article at Heritage Science.
Version 2
Posted 08 Dec, 2020
No community comments so far
Editorial decision: Accept
On 21 Dec, 2020
Review #1 received
Received 02 Dec, 2020
Reviewer #2 agreed
On 29 Nov, 2020
Reviewer #1 agreed
On 29 Nov, 2020
Reviewers invited
Invitations sent on 28 Nov, 2020
Editor assigned
On 26 Nov, 2020
Submission checks complete
On 26 Nov, 2020
Editor invited
On 26 Nov, 2020
No comments provided
Editorial decision: Major revision
On 30 Aug, 2020
Review #2 received
Received 28 Aug, 2020
Reviewer #2 agreed
On 13 Aug, 2020
Review #1 received
Received 31 Jul, 2020
Reviewer #1 agreed
On 28 Jul, 2020
Reviewers invited
Invitations sent on 27 Jul, 2020
Editor assigned
On 24 Jul, 2020
First submitted
On 23 Jul, 2020
Submission checks complete
On 23 Jul, 2020
Editor invited
On 23 Jul, 2020
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at Heritage Science.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Heritage Science.
Version 2
Posted 08 Dec, 2020
No community comments so far
Editorial decision: Accept
On 21 Dec, 2020
Review #1 received
Received 02 Dec, 2020
Reviewer #2 agreed
On 29 Nov, 2020
Reviewer #1 agreed
On 29 Nov, 2020
Reviewers invited
Invitations sent on 28 Nov, 2020
Editor assigned
On 26 Nov, 2020
Submission checks complete
On 26 Nov, 2020
Editor invited
On 26 Nov, 2020
No comments provided
Editorial decision: Major revision
On 30 Aug, 2020
Review #2 received
Received 28 Aug, 2020
Reviewer #2 agreed
On 13 Aug, 2020
Review #1 received
Received 31 Jul, 2020
Reviewer #1 agreed
On 28 Jul, 2020
Reviewers invited
Invitations sent on 27 Jul, 2020
Editor assigned
On 24 Jul, 2020
First submitted
On 23 Jul, 2020
Submission checks complete
On 23 Jul, 2020
Editor invited
On 23 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Heritage Science.
Shenzhen is a modern metropolis, but it hides a variety of valuable cultural heritage, such as ancient murals. How to effectively preserve and repair the murals is a worthy of discussion question. Here, we propose a generation-discriminator network model based on artificial intelligence algorithms to perform digital image restoration of ancient damaged murals. In adversarial learning, this study optimizes the discriminative network model. First, the real mural images and damaged images are spliced together as input to the discriminator network. The network uses a 5-layer encoder unit to down-sample the 1024 x 1024 x 3 image to 32 x 32 x 256. Then, we connect a layer of ZeroPadding2D to expand the image to 34 x 34 x 256, and pass the Conv2D layer, down-sample to 31 x 31 x 256, perform batch normalization, and repeat the above steps to get a 30 x 30 x 1 matrix. Finally, this part of the loss is emphasized in the loss function as needed to improve the texture detail information of the image generated by the Generator. The experimental results show that compared with the traditional algorithm, the PSNR value of the algorithm proposed in this paper can be increased by 5.86 db at most. The SSIM value increased by 0.13. Judging from subjective vision. The proposed algorithm can effectively repair damaged murals with dot-like damage and complex texture structures. The algorithm we proposed may be helpful for the digital restoration of ancient murals, and may also provide reference for mural restoration workers.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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