  Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a
perceptual-driven approach for single image super resolution that is able to
produce photorealistic images. Despite the visual quality of these generated
images, there is still room for improvement. In this fashion, the model is
extended to further improve the perceptual quality of the images. We have
designed a novel block to replace the one used by the original ESRGAN.
Moreover, we introduce noise inputs to the generator network in order to
exploit stochastic variation. The resulting images present more realistic
textures. The code is available at https://github.com/ncarraz/ESRGANplus .
