《CFSNetTowardaControlledFeatureSpaceforImageRestoration》的pyt.zip

# CFSNet pytorch code of "CFSNet: Toward a Controllable Feature Space for Image Restoration"（ICCV2019） #### [[Paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_CFSNet_Toward_a_Controllable_Feature_Space_for_Image_Restoration_ICCV_2019_paper.pdf)][[arXiv](https://arxiv.org/abs/1904.00634)][[Poster](https://github.com/qibao77/CFSNet/blob/master/CFSNet_poster.pdf)] <div align=center><img width="360" height="240" src="https://github.com/qibao77/CFSNet/blob/master/figs/coupling_module.png"/></div> Coupling module architecture.  The framework of our proposed controllable feature space network (CFSNet). The details about our proposed CFSNet can be found in [our main paper](https://arxiv.org/abs/1904.00634). If you find our work useful in your research or publications, please consider citing: ```latex @InProceedings{Wang_2019_ICCV, author = {Wang, Wei and Guo, Ruiming and Tian, Yapeng and Yang, Wenming}, title = {CFSNet: Toward a Controllable Feature Space for Image Restoration}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, month = {October}, year = {2019} } @article{wang2019cfsnet, title={CFSNet: Toward a Controllable Feature Space for Image Restoration}, author={Wang, Wei and Guo, Ruiming and Tian, Yapeng and Yang, Wenming}, journal={arXiv preprint arXiv:1904.00634}, year={2019} } ``` ## Contents 1. [Illustration](#illustration) 2. [Testing](#testing) 3. [Training](#training) 4. [Results](#results) ## Illustration The overall code framework: ``` CFSNet ── codes └── data ├──deblock_dataset.py ├──denoise_dataset.py ├──sr_dataset.py └── util.py ├──models └── modules ├── architecture.py ├── block.py ├── loss.py ├── main_net.py └── tuning_blocks.py ├── CFSNet_model.py ├──settings ├── test ├── train └──options.py ├──utils ├── logger.py └── util.py ├── train.py └── test.py ``` #### Dependencies and Installation GPU: cuda 8.0. Python 3.6.3 (Recommend to use Anaconda) Packages: ``` pip install torch-0.4.1-cp36-cp36m-manylinux1_x86_64.whl pip install torchvision-0.2.1-py2.py3-none-any.whl pip install opencv-python pip install opencv-contrib-pytho pip install lmdb pip install scikit_image-0.13.1-cp36-cp36m-manylinux1_x86_64.whl pip install tensorboard-logger ``` (some .whl packages can be found here: [Google Cloud Disk](https://drive.google.com/drive/folders/1uYOMtNC_xYw9OGuaAs3COAg3fUURdSbH?usp=sharing)) ## Testing We provide some trained models of CFSNet, and you can download them from [Google Cloud Disk](https://drive.google.com/drive/folders/12O5FDgZ99jdbyeyLeAgiM-4ygsoAeq0l?usp=sharing). 1. Prepare datasets according to task type. (Some datasets can be found in [Google Cloud Disk](https://drive.google.com/open?id=1C0VjWZr2dVQsPyN9jXzGxR-Y0iR9oX0A).) 2. Modify the configuration file (settings/test/*.json) according to your personal situation. (please refer to ['settings/test'](settings/test) for instructions.) 3. Run following command for evaluation: ```python python test.py -opt settings/test/*.json ``` ## Training 1. Prepare training datasets according to task type. 2. Modify the configuration file (settings/test/*.json) according to your personal situation. (Please refer to ['settings/train'](settings/train) for instructions.) 3. Run the following command for training: ```python python train.py -opt settings/train/*.json ``` Note: Our training process consists of two steps, first training the main branch, then training the tuning branch. Therefore, you need to modify the configutation file at different training stages. ## Results #### Visual Results  Perceptual and distortion balance of “215”, “211” and “268” (PIRM test dataset) for 4× image super-resolution. <div align=center><img width="200" height="200" src="https://github.com/qibao77/CFSNet/blob/master/figs/sr_crop.gif"/></div> Smooth control of distortion and perceptual with control scalar α_in from 0 to 1.  Color image denoising results with unknown noise level σ = 15 (first two rows) and σ = 40 (last two rows). α_in = 0.5 and α_in = −0.3 correspond to the highest PSNR results, respectively. <div align=center><img width="200" height="200" src="https://github.com/qibao77/CFSNet/blob/master/figs/denoise_color_flower_crop.gif"/></div> Smooth control of noise reduction and detail preservation with control scalar α_in from 0 to 1.  JPEG image artifacts removal results of “house” and “ocean” (LIVE1) with unknown quality factor 20. α_in = 0.5 corresponds to the highest PSNR results, and the best visual results are marked with red boxes.  Visual results of single image super-resolution with unseen degradation (the blur kernel is 7 × 7 Gaussian kernel with standard deviation 1.6, the scale factor is 3). α_in = 0.15 corresponds to the highest PSNR results. <div align=center><img width="200" height="200" src="https://github.com/qibao77/CFSNet/blob/master/figs/deblur_tiger_crop.gif"/></div> Smooth control of blur removal and detail sharpening with control scalar α_in from 0 to 0.8. ## Acknowledgement Thank [BasicSR](https://github.com/xinntao/BasicSR). They provide many useful codes which facilitate our work.