Ming Zhao1, Xinhong Liu1, Xin Yao1, *, Kun He2
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1601-1614, 2020, DOI:10.32604/cmc.2020.09754
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… More >
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