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Image Super-Resolution Using Very Deep Residual Channel Attention Networks

An implementation of RCAN described in the paper using tensorflow. Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Published in ECCV 2018, written by Y. Zhang, K. Li, L. Wang, B. Zhong, and Y. Fu

Requirement

  • Python 3.6.5
  • Tensorflow 1.13.1
  • Pillow 6.0.0
  • numpy 1.15.0
  • scikit-image 0.15.0

Datasets

Pre-trained model

Train using your own dataset

python main.py --train_GT_path ./GT_path --train_LR_path ./LR_path --test_GT_path ./test_GT_path --test_LR_path ./test_LR_path --test_with_train True --scale 2(or 3, 4, ...) --log_freq 1000
  • LR image and HR image pair should have same index when they are sorted by name respectively.
  • You can refer to the script file (run.sh) in my repository

Test using benchmarks

  1. Download pre-trained model.
  1. Unzip the pre-trained model file
tar -cvf model.tar
  1. Test using benchmarks
python main.py --mode test --pre_trained_model ./model/RCAN_X2(or 3, 4) --test_LR_path ./benchmark_LR_path --test_GT_path ./benchmark_GT_path --scale 2(or 3, 4) --self_ensemble False

If you want to use self_ensemble, --self_ensemble option to True

  • You can refer to the script file (run.sh) in my repository

Inference your own images

  1. Download pre-trained model.
  1. Unzip the pre-trained model file
tar -cvf model.tar
  1. Inference your own images
python main.py --mode test_only --pre_trained_model ./model/RCAN_X2(or 3, 4) --test_LR_path ./your_own_images --scale 2(or 3, 4) --chop_forward False

If your images are too large, OOM error can occur. In that case, --chop_forward option to True

Experimental Results

Qunatitative Results

Method Scale Set5 Set14 B100 Urban100
Bicubic X2 33.66 / 0.9299 30.24 / 0.8688 29.56 / 0.8431 26.88 / 0.8403
RDN X2 38.24 / 0.9614 34.01 / 0.9212 32.34 / 0.9017 32.89 / 0.9353
RCAN(paper) X2 38.27 / 0.9614 34.12 / 0.9216 32.41 / 0.9027 33.34 / 0.9384
RCAN(my results) X2 38.25 / 0.9615 34.07 / 0.9216 32.36 / 0.9020 33.12 / 0.9367
Method Scale Set5 Set14 B100 Urban100
Bicubic X3 30.39 / 0.8682 27.55 / 0.7742 27.21 / 0.7385 24.46 / 0.7349
RDN X3 34.71 / 0.9296 30.57 / 0.8468 29.26 / 0.8093 28.80 / 0.8653
RCAN(paper) X3 34.74 / 0.9299 30.65 / 0.8482 29.32 / 0.8111 29.09 / 0.8702
RCAN(my results) X3 34.75 / 0.9302 30.61 / 0.8470 29.31 / 0.8105 29.03 / 0.8693
Method Scale Set5 Set14 B100 Urban100
Bicubic X4 28.42 / 0.8104 26.00 / 0.7027 25.96 / 0.6675 23.14 / 0.6577
RDN X4 32.47 / 0.8990 28.81 / 0.7871 27.72 / 0.7419 26.61 / 0.8028
RCAN(paper) X4 32.63 / 0.9002 28.87 / 0.7889 27.77 / 0.7436 26.82 / 0.8087
RCAN(my results) X4 32.56 / 0.8996 28.89 / 0.7891 27.78 / 0.7434 26.81 / 0.8079

Qualitative results are will be updated soon!

Comments

If you have any questions or comments on my codes, please email to me. son1113@snu.ac.kr

Reference

[1] https://github.com/yulunzhang/RCAN