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VESPCN-PyTorch
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VESPCN-PyTorch
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# VESPCN-PyTorch PyTorch implementation of ESPCN [1]/VESPCN [2]. ## **How to run the code** 1. Add your own template in template.py, indicating parameters related to running the code (especially, specify the task (Image/MC/Video) and set training/test dataset directories specific to your filesystem) 2. Add your model in ./model/ directory (filename should be in lower cases) 3. Type "python3 main.py --template $(your template) --model $(model you want to train)" for training 4. If you want to add additional options for test benchmark datasets, modify ./data/__init__.py. 5. For additional details, refer to [3] (We have borrowed most of the implementation details from there). ## **TODO list** - [x] Implement the SISR ESPCN network - [x] Making dataloader for video SR - [x] Complete the motion compensation network - [x] Joining the ESPCN to motion compensation network ## **References** [1] W. Shi et al, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” IEEE CVPR 2016. [2] J. Caballero et al, “Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation,” IEEE CVPR 2017. [3] https://github.com/thstkdgus35/EDSR-PyTorch (borrowed the overall code structure)
