Image Super-resolution via Dual-state Recurrent Neural Networks (CVPR 2018), Tensorflow 2

[Paper Link]

Citation

@inproceedings{han2018image,  
	title={Image super-resolution via dual-state recurrent networks},
	author={Han, Wei and Chang, Shiyu and Liu, Ding and Yu, Mo and Witbrock, Michael and Huang, Thomas S},
	booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
	year={2018}
}

Dependencies

• Common python dependencies can be installed via pip install -r requirements.txt
• Lingvo is no longer required for weights

Data

There is a very helpful repo collected download links for all the training and test sets needed here.

Training

The training data is specified by a file list of HR images. No futher pre-processing is needed as we perform downsampling and augmentation on-the-fly.

Use train.py and the model specification file model_recurrent_s2_u128_avg_t7.py to start a training job.

Inference

Models are not provided, and must be trained by the user using the train.py file.

Evaluation

Use evaluate.py to compute average PSNR on a test set after saving all the model predicted images.

Acknowledgement

This code is partly based on a previous work from the group [here], as well as the original DSRN project