**NEWS **: A improved CrossNet++ is published in TPAMI and the code can be found at https://github.com/THU-luvision/CrossNetPP
This repository contains the pyTorch implementation of ECCV2018 paper ''CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping''. Note that we use a batchsize of 4 instead of 2, and a slightly different input for flownet (see usage 1), results with ~0.2dB higher PSNR is achieved.
We would like to thank Yang Tan (https://github.com/tanyang1231) for implementating the code using pyTorch.
pytorch opencv h5py
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prepare an hdf5 file, which contains /img_HR, /img_LR, /img_MDSR, /img_LR_upsample. /img_HR is used as reference input and ground truth, /img_LR is used as low resolution input, /img_MDSR is the MDSR upsampled image, and /img_LR_upsample is bicubically upsampled image. (Different from the original paper, in this version of code, we use Flownet with bicubically upsampled image and reference image to generate optical flow)
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mkdir checkpoints_charbonnier_3
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python -u train_multi_warping.py --dataset Flower --display 50 --batch_size 2 --step_size 150000 --loss CharbonnierLoss --optim Adam --lr 0.0001 --checkpoints_dir ./checkpoints_charbonnier_3/ --checkpoint 195000 --checkpoint_file ./checkpoints_charbonnier_3/CP195000.pth
Please cite our paper if you find it interesting!
@inproceedings{zheng2018crossnet,
title={CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping},
author={Zheng, Haitian and Ji, Mengqi and Wang, Haoqian and Liu, Yebin and Fang, Lu},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={88--104},
year={2018}
}