Using a Two-Stage GAN to Learn Image Degradation for Image Super-Resolution-Reference-Cited by-同舟云学术

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Using a Two-Stage GAN to Learn Image Degradation for Image Super-Resolution

Published:2021 Issue: Volume: Page:222-230
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Cheng Jiarui,Jiang Ning,Tang Jialiang,Deng Xin,Yu Wenxin,Zhang Peng

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-92307-5_26

Reference13 articles.

1. Lecture Notes in Computer Science;A Bulat,2018

2. Chen, Y., Tai, Y., Liu, X., Shen, C., Yang, J.: FSRNet: end-to-end learning face super-resolution with facial priors. In: CVPR (2018)

3. Fu, J., et al.: Dual attention network for scene segmentation. In: CVPR (2019)

4. Huang, H., He, R., Sun, Z., Tan, T.: Wavelet-SRNet: a wavelet-based CNN for multi-scale face super resolution. In: ICCV (2017)

5. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)

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1. Algorithm for Generating Tire Defect Images Based on RS-GAN;Communications in Computer and Information Science;2023-11-27

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