2020
DOI: 10.1155/2020/6670976
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Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals

Abstract: 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 recons… Show more

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Cited by 4 publications
(2 citation statements)
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“…Cao et al introduced an algorithm for super-resolution reconstruction of art mural images [6]. The structure of the algorithm is divided into two parts: generation network and discriminant network.…”
Section: Related Workmentioning
confidence: 99%
“…Cao et al introduced an algorithm for super-resolution reconstruction of art mural images [6]. The structure of the algorithm is divided into two parts: generation network and discriminant network.…”
Section: Related Workmentioning
confidence: 99%
“…shapes [8]. Aiming at the problem of poor generalization ability of current 3D reconstruction technology, an improved algorithm model based on the Marr network model was proposed.…”
Section: Network Model Structure By Learning Certain Functions the Re...mentioning
confidence: 99%