Official Pytorch implementation of "Multi-Stage Edge-Guided Stereo Feature Interaction Network for Stereoscopic Image Super-Resolution" paper.
The architecture of our proposed Multi-Stage Edge-Guided Stereo Feature Interaction Network.
Citation:
@ARTICLE{10121360,
author={Wan, Jin and Yin, Hui and Liu, Zhihao and Liu, Yanting and Wang, Song},
journal={IEEE Transactions on Broadcasting},
title={Multi-Stage Edge-Guided Stereo Feature Interaction Network for Stereoscopic Image Super-Resolution},
year={2023},
volume={69},
number={2},
pages={357-368},
doi={10.1109/TBC.2023.3264880}}
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Run the following scripts to train models. You can use scripts in the file 'demo' to train models for our paper.
# BI, scale 2, 4 # MESFINet in the paper (x2) # CUDA_VISIBLE_DEVICES=3,4 python train.py --model_name MESFINet --scale_factor 2 --checkpoints_dir ./log \ # --edge_model_path ./pretrained_model/bdcn_pretrained_on_nyudv2_rgb.pth # MESFINet in the paper (x4) - from MESFINet (x2) # CUDA_VISIBLE_DEVICES=3,4 python train.py --model_name MESFINet --scale_factor 4 --checkpoints_dir ./log --edge_model_path ./pretrained_model/bdcn_pretrained_on_nyudv2_rgb.pth \ # --load_pretrain_modelx2 True --modelx2_path ./pretrained_model/MESFINet_2xSR_final.pth.tar
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Clone this repository:
git clone https://github.com/jinwan1994/MESFINet.git
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All the models (BIX2/4) can be downloaded from GoogleYun, place the models to
./pretrained_model/
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Run the following scripts.
You can use scripts in the file 'demo' to produce results for our paper.
# BI, scale 2, 4 # Standard benchmarks (Ex. MESFINetx2) # python test.py --model_name MESFINet_2xSR --scale_factor 2 --save_dir ./results_test --sr_model_path ./pretrained_model/MESFINet_2xSR_final.pth.tar \ # --dataset_list KITTI2012+KITTI2015+Middlebury --edge_model_path ./pretrained_model/bdcn_pretrained_on_nyudv2_rgb.pth # Standard benchmarks (Ex. MESFINet_x4) python test.py --model_name MESFINet_4xSR --scale_factor 4 --save_dir ./results_test --sr_model_path ./pretrained_model/MESFINet_4xSR_final.pth.tar \ --dataset_list KITTI2012+KITTI2015+Middlebury --edge_model_path ./pretrained_model/bdcn_pretrained_on_nyudv2_rgb.pth
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Finally, SR results and PSNR/SSIM values for test data are saved to
./results_test/*
. (PSNR/SSIM values in our paper are obtained using Matlab2021)
Benchmark SR results. Average PSNR/SSIM for scale factor x2 and x4 on four datasets.
Visual comparison for 2x SR on two datasets.