Open Access
ARTICLE
Better Visual Image Super-Resolution with Laplacian Pyramid of Generative Adversarial Networks
Ming Zhao1, Xinhong Liu1, Xin Yao1, *, Kun He2
1 School of Computer Science and Engineering, Central South University, Changsha, 410000, China.
2 Apple Inc., California, 95014, USA.
* Corresponding Author: Xin Yao. Email: .
Computers, Materials & Continua 2020, 64(3), 1601-1614. https://doi.org/10.32604/cmc.2020.09754
Received 17 January 2020; Accepted 03 March 2020; Issue published 30 June 2020
Abstract
Although there has been a great breakthrough in the accuracy and speed of
super-resolution (SR) reconstruction of a single image by using a convolutional neural
network, an important problem remains unresolved: how to restore finer texture details
during image super-resolution reconstruction? This paper proposes an Enhanced
Laplacian Pyramid Generative Adversarial Network (ELSRGAN), based on the
Laplacian pyramid to capture the high-frequency details of the image. By combining
Laplacian pyramids and generative adversarial networks, progressive reconstruction of
super-resolution images can be made, making model applications more flexible. In order
to solve the problem of gradient disappearance, we introduce the Residual-in-Residual
Dense Block (RRDB) as the basic network unit. Network capacity benefits more from
dense connections, is able to capture more visual features with better reconstruction
effects, and removes BN layers to increase calculation speed and reduce calculation
complexity. In addition, a loss of content driven by perceived similarity is used instead of
content loss driven by spatial similarity, thereby enhancing the visual effect of the superresolution image, making it more consistent with human visual perception. Extensive
qualitative and quantitative evaluation of the baseline datasets shows that the proposed
algorithm has higher mean-sort-score (MSS) than any state-of-the-art method and has
better visual perception.
Keywords
Cite This Article
M. Zhao, X. Liu, X. Yao and K. He, "Better visual image super-resolution with laplacian pyramid of generative adversarial networks,"
Computers, Materials & Continua, vol. 64, no.3, pp. 1601–1614, 2020.
Citations