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Lightweight Image Super-Resolution with Information Multi-distillation Network

Lightweight Image Super-Resolution with Information Multi-distillation Network

IMDN

Lightweight Image Super-Resolution with Information Multi-distillation Network (ACM MM 2019)

ICCV 2019 Workshop AIM

The simplified version of IMDN won the first place at Contrained Super-Resolution Challenge.

Hightlights

  1. Our information multi-distillation block (IMDB) with contrast-aware attention (CCA) layer.

  2. The adaptive cropping strategy (ACS) to achieve the processing images of any arbitrary size (implementing any upscaling factors using one model).

  3. The exploration of factors affecting actual inference time.

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/IMDN_x2.pth --upscale_factor 2
# RealSR IMDN_AS
python test_IMDN_AS.py --test_hr_folder Test_Datasets/RealSR/ValidationGT --test_lr_folder Test_Datasets/RealSR/ValidationLR/ --output_folder results/RealSR --checkpoint checkpoints/IMDN_AS.pth

Results

百度网盘取码: 8yqj or
Google drive

The following PSNR/SSIMs are evaluated on Matlab R2017a and the code can be referred to Evaluate_PSNR_SSIM.m.

PSNR & SSIM

psnr_ssim

Average PSNR/SSIM on datasets Set5, Set14, BSD100, Urban100, and Manga109.

Memory consumption

memory

Memory Consumption (MB) and average inference time (second).

Model parameters

parameters

Trade-off between performance and number of parameters on Set5 ×4 dataset.

Running time

time
Trade-off between performance and running time on Set5 ×4 dataset. VDSR, DRCN, and LapSRN were implemented by MatConvNet, while DRRN, and IDN employed Caffe package. The rest EDSR-baseline, CARN, and our IMDN utilized PyTorch.

Adaptive Cropping

adaptive_cropping

The diagrammatic sketch of adaptive cropping strategy (ACS). The cropped image patches in the green dotted boxes.

Visualization of feature maps

lenna

Visualization of output feature maps of the 6-th progressive refinement module (PRM).

Citation

If you find IMDN useful in your research, please consider citing:

@inproceedings{Hui-IMDN-2019,
  title={Lightweight Image Super-Resolution with Information Multi-distillation Network},
  author={Hui, Zheng and Gao, Xinbo and Yang, Yunchu and Wang, Xiumei},
  booktitle={ACM MM},
  year={2019}
}

@inproceedings{AIM19constrainedSR,
  title={AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results},
  author={Kai Zhang and Shuhang Gu and Radu Timofte and others},
  booktitle={The IEEE International Conference on Computer Vision (ICCV) Workshops},
  year={2019}
}

GitHub