Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals
المؤلفون المشاركون
Cao, Jianfang
Zhang, Zibang
Zhao, Aidi
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-29
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138796
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر