Transcription of ESRGAN: Enhanced Super-Resolution Generative Adversarial ...
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ESRGAN: Enhanced Super-Resolution Generative Adversarial ...
ESRGAN: Enhanced Super-ResolutionGenerative Adversarial NetworksXintao Wang1, Ke Yu1, Shixiang Wu2, Jinjin Gu3, Yihao Liu4,Chao Dong2, Yu Qiao2, and Chen Change Loy51 CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences3 The Chinese University of Hong Kong, Shenzhen4 University of Chinese Academy of Sciences5 Nanyang Technological University, Super-Resolution Generative Adversarial Network (SR-GAN) is a seminal work that is capable of generating realistic texturesduring single image Super-Resolution . However, the hallucinated detailsare often accompanied with unpleasant artifacts. To further enhancethe visual quality, we thoroughly study three key components of SR-GAN network architecture, Adversarial loss and perceptual loss,andimprove each of them to derive an Enhanced SRGAN (ESRGAN). Inparticular, we introduce the Residual-in-Residual Dense Block(RRDB)without batch normalization as the basic network building unit.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks 3 Perceptual Index RMSE ESRGAN EnhanceNet RCAN EDSR R1 R2 EDSR RCAN ESRGAN EnhanceNet Method PI RMSE 2.040 15.15 2.688 5.243 15.99 10.87 11.16 4.831 Results on PIRM self val dataset interp_1 interp_2 R3 interp_2 2.567 12.45 interp_1 3.279 11.47
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