ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network

Nathanaël Carraz Rakotonirina, Andry Rasoanaivo

Abstract: 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 network architecture with a novel basic 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.