The State-of-the-Art (SOTA) models being used for this project:
1. Real-ESRGAN (Real Enhanced Super-Resolution Generative Adversarial Network)
2. GFPGAN (Generative Facial Pre-trained Generative Adversarial Network) version 1.4
Original | Real-ESRGANx2 | Real-ESRGANx4 | Real-ESRGANx4 + GFPGAN Face Enhancement |
One can choose between 2 options to enhance their image: a 2-layered NN version (RealESRGAN_x2plus.pth) or a 4-layered NN version (RealESRGAN_x4plus.pth). This means that the 4-layered model passes the original image through 4 layers of neural networks compared to the 2-layered model which only passes the original image through 2 layers of neural networks. A general rule will be that the more NN layers our image passes through means the enhancement is much more perfect. But at the same time, this means that running the x4layer model takes more time than the x2layer model.
*_Note: If your image contains repeated textures or patters, using the 2-layers over the 4-layers may sometimes produce more satisfactory results._**
View the Google Colab notebook for inferencing and enhancing your images here! Make a copy of it and get started! 😜