Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals
Joint Authors
Cao, Jianfang
Zhang, Zibang
Zhao, Aidi
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-29
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Considering the problems of low resolution and rough details in existing mural images, this paper proposes a superresolution reconstruction algorithm for enhancing artistic mural images, thereby optimizing mural images.
The algorithm takes a generative adversarial network (GAN) as the framework.
First, a convolutional neural network (CNN) is used to extract image feature information, and then, the features are mapped to the high-resolution image space of the same size as the original image.
Finally, the reconstructed high-resolution image is output to complete the design of the generative network.
Then, a CNN with deep and residual modules is used for image feature extraction to determine whether the output of the generative network is an authentic, high-resolution mural image.
In detail, the depth of the network increases, the residual module is introduced, the batch standardization of the network convolution layer is deleted, and the subpixel convolution is used to realize upsampling.
Additionally, a combination of multiple loss functions and staged construction of the network model is adopted to further optimize the mural image.
A mural dataset is set up by the current team.
Compared with several existing image superresolution algorithms, the peak signal-to-noise ratio (PSNR) of the proposed algorithm increases by an average of 1.2–3.3 dB and the structural similarity (SSIM) increases by 0.04 = 0.13; it is also superior to other algorithms in terms of subjective scoring.
The proposed method in this study is effective in the superresolution reconstruction of mural images, which contributes to the further optimization of ancient mural images.
American Psychological Association (APA)
Cao, Jianfang& Zhang, Zibang& Zhao, Aidi. 2020. Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138796
Modern Language Association (MLA)
Cao, Jianfang…[et al.]. Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1138796
American Medical Association (AMA)
Cao, Jianfang& Zhang, Zibang& Zhao, Aidi. Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138796
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138796