State of the art in single image super resolution! For project and code/API/exp

State of the art in single image super resolution!

For project and code/API/expert requests: https://www.catalyzex.com/paper/arxiv:2007.04344

Excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a Super Resolution model, which limits their applications to Super Resolution with resource constrained devices in real world. To resolve these problems, researchers propose a lightweight enhanced Super Resolution convolutional neural network