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Content Provider | IET Digital Library |
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Author | Gao, Hongxia Chen, Zhanhong Huang, Binyang Chen, Jiahe Li, Zhifu |
Abstract | Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high-frequency details. However, a mismatch between the input and the output may arise when GAN is directly applied to image super-resolution. To alleviate this issue, the authors adopted a conditional GAN (cGAN) in this study. The cGAN discriminator attempted to guess whether the unknown high-resolution (HR) image was produced by the generator with the aid of the original low-resolution (LR) image. They propose a novel discriminator that only penalises at the scale of the patch and, thus, has relatively few parameters to train. The generator of cGAN is an encoder–decoder with skip connections to shuttle the shared low-level information directly across the network. To better maintain the low-frequency information and recover the high-frequency information, they designed a generator loss function combining adversarial loss term and L1 loss term. The former term is beneficial to the synthesis of fine-grained textures, while the latter is responsible for learning the overall structure of the LR input. The experiments revealed that the proposed method could generate HR images with richer details and less over-smoothness. |
Starting Page | 3006 |
Ending Page | 3013 |
Page Count | 8 |
ISSN | 17519659 |
Volume Number | 14 |
e-ISSN | 17519667 |
Issue Number | Issue 13, Nov (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/14/13 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5767 |
Journal | IET Image Processing |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2020-05-26 |
Access Restriction | Open |
Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Adversarial Loss Term CGAN Discriminator Computer Vision And Image Processing Technique Conditional GAN Conditional Generative Adversarial Network Generator Loss Function High-frequency Information High-resolution Image HR Image Image Resolution Image Super-resolution L1 Loss Term Low-frequency Information Low-resolution Image Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Unsupervised Learning |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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