Operation-wise-attention-network
Operation-wise-attention-network copied to clipboard
Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions (CVPR 2019)
Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions
This repository contains the code for the following paper:
Masanori Suganuma, Xing Liu, Takayuki Okatani, "Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions," CVPR, 2019. [arXiv]
If you find this work useful in your research, please cite:
@inproceedings{suganumaCVPR2019,
Author = {M. Suganuma and X. Liu and T. Okatani},
Title = {Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions},
Booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
Year = {2019}
}
Sample results on image restoration:
Sample results on object detection:
Requirement
- Ubuntu 16.04 LTS
- CUDA version 10.0
- Python version 3.6.2
- PyTorch version 1.0
Usage
Train a model on the dataset proposed by RL-Restore
python main.py -m mix -g 1
When you use the multiple GPUs, please specify the number of gpus by -g
option (default:1)
Dataset
The dataset used in RL-Restore is available here.
To generate the training dataset, please run data/train/generate_train.m
in the repository and put the generated file (train.h5) to dataset/train/
in your computer.
Train a model on your own dataset
python main.py -m yourdata -g 1
Test
Put the trained model (XXXX.pth) to Trained_model/
, and run the following code:
python test.py -m mix -g 1