HAT
The HAT project showcases a novel method for image restoration with emphasis on super-resolution. Utilizing advanced pixel activation, it enhances image quality on datasets like Set5, Set14, and Urban100, independent of ImageNet pretraining. The project includes GAN-based models tailored for sharper and more accurate results. Discover comprehensive performance insights through the available codes and pre-trained models, alongside straightforward testing and training guidance for practical application in real-world scenarios of image super-resolution.
