Final Project for MIT 6.819 Advances in Computer Vision
pytorch skimage numpy cv2 tqdm
This project is based on the following two papers.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Frame-Recurrent Video Super-Resolution
We are trying to combine these two models together in our project (this is the where the name come from "Frame Recurrent Super Resolution GAN").
Our final report is available here: Report
We used the dataset from toflow-dataset, which is about 15GB, containing 7.8k video clips with 7 frames per clip.
We trained our model on the dataset for 9 epochs. and compared with default SRGAN model in the repo. We did not retrain SRGAN in our dataset, so the result is for reference only.
The following results are produced by 7-epoch model.
We referred to SRGAN Implementation by LeftThomas in our project. We would like to thank the author for his fabulous work.