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ESRT

Efficient Transformer for Single Image Super-Resolution

Update

#######22.03.17########

The result images of our method are collected in fold "/result".

Environment

  • pytorch >=1.0
  • python 3.6
  • numpy

Model


The overall architecture of the proposed Efficient SR Transformer (ESRT).


Efficient Transformer and Efficient Multi-Head Attention.

Train

  • dataset: DIV2K

  • prepare

    Like IMDN, convert png files in DIV2K to npy files:

    python scripts/png2npy.py --pathFrom /path/to/DIV2K/ --pathTo /path/to/DIV2K_decoded/

  • Training

python train.py --scale 2 --patch_size 96
python train.py --scale 3 --patch_size 144
python train.py --scale 4 --patch_size 192

If you want a better result, use 128/192/256 patch_size for each scale.

Test

Example:

  • test B100 X4
python test.py --is_y --test_hr_folder dataset/benchmark/B100/HR/ --test_lr_folder dataset/benchmark/B100/LR_bicubic/X4/ --output_folder results/B100/x4 --checkpoint experiment/checkpoint/x4/epoch_990.pth --upscale_factor 4

Visual comparison


The visual comparison.