MBMFN

Our algorithm is based on IMDN（Lightweight Image Super-Resolution with Information Multi-distillation Network)

#IMDN： [arXiv] [Poster] [ACM DL]

ICCV 2019 Workshop AIM report

The simplified version of IMDN won the first place at Contrained Super-Resolution Challenge (Track1 & Track2). The test code is available at Google Drive

Special statement: Because our algorithm is based on the IDMN algorithm, we didn't change the name in the code, for example, python test_IMDN.py, we didn't change the file name of test_IMDN.py. We just use their names, the rest of the content is completely different from IDMN.

Testing

Pytorch 1.1

• Runing testing:

# Set5 x2 IMDN
python test_IMDN.py --test_hr_folder Test_Datasets/Set5/ --test_lr_folder Test_Datasets/Set5_LR/x2/ --output_folder results/Set5/x2 --checkpoint checkpoints/epoch_x2.pth --upscale_factor 2

• Calculating FLOPs and parameters, input size is 192*192

python calc_FLOPs.py

Training

• Download Training dataset DIV2K
• Convert png file to npy file

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

• Run training x2, x3, x4 model Put '' DIV2K_decoded'' in the current folder

python train_IMDN.py --root ./ --scale 2 --pretrained checkpoints/IMDN_x2.pth