ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
This is an incremental improvement for applications requiring photorealistic image enhancement, such as in media or computer vision.
The paper tackles the problem of improving perceptual quality in single image super-resolution by extending ESRGAN with a novel block and noise inputs, resulting in images with more realistic textures.
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 .