{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/esrgan-enhanced-super-resolution-generative","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","arxiv_id":"1809.00219","date":"2018-09-01","proceeding":null,"authors":["Xintao Wang","Ke Yu","Shixiang Wu","Jinjin Gu","Yihao Liu","Chao Dong","Chen Change Loy","Yu Qiao","Xiaoou Tang"],"abstract":"The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work\nthat is capable of generating realistic textures during single image\nsuper-resolution. However, the hallucinated details are often accompanied with\nunpleasant artifacts. To further enhance the visual quality, we thoroughly\nstudy three key components of SRGAN - network architecture, adversarial loss\nand perceptual loss, and improve each of them to derive an Enhanced SRGAN\n(ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block\n(RRDB) without batch normalization as the basic network building unit.\nMoreover, we borrow the idea from relativistic GAN to let the discriminator\npredict relative realness instead of the absolute value. Finally, we improve\nthe perceptual loss by using the features before activation, which could\nprovide stronger supervision for brightness consistency and texture recovery.\nBenefiting from these improvements, the proposed ESRGAN achieves consistently\nbetter visual quality with more realistic and natural textures than SRGAN and\nwon the first place in the PIRM2018-SR Challenge. The code is available at\nhttps://github.com/xinntao/ESRGAN .","url_abs":"http://arxiv.org/abs/1809.00219v2","url_pdf":"http://arxiv.org/pdf/1809.00219v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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