ESRGAN is an upscaling method that is capable of generating realistic textures during single image… Created by Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy (Description from https://www.resetera.com/threads/ai-neural-networks-being-used-to-generate-hq-textures-for-older-games-you-can-do-it-yourself.88272/ )Enhanced Super Resolution Generative Adverserial Network, or ESRGAN, is an upscaling method that is capable of generating realistic textures during single image super-resolution. Basically it’s a machine learning technique that uses a generative adverserial network to upres smaller images. By doing it over several passes, it will usually produce an image with more fidelity than methods such as SRCNN and SRGAN. In fact, ESRGAN is based off SRGAN. The difference between the two is that ESRGAN improves on SRGAN’s network architecture, adversarial loss and perceptual loss. Furthermore ESRGAN adopts a deeper model using Residual-in-Residual Dense Block (RRDB) without batch normalization layers, and employs Relativistic average GAN instead of the vanilla GAN….
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