视频去模糊的对抗性时空学习

# DeblurGAN [arXiv Paper Version](https://arxiv.org/pdf/1711.07064.pdf) Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. Our network takes blurry image as an input and procude the corresponding sharp estimate, as in the example: <img src="images/animation3.gif" width="400px"/> <img src="images/animation4.gif" width="400px"/> The model we use is Conditional Wasserstein GAN with Gradient Penalty + Perceptual loss based on VGG-19 activations. Such architecture also gives good results on other image-to-image translation problems (super resolution, colorization, inpainting, dehazing etc.) ## How to run ### Prerequisites - NVIDIA GPU + CUDA CuDNN (CPU untested, feedback appreciated) - Pytorch Download weights from [Dropbox](https://www.dropbox.com/s/5r6cy0x72s8x9yf/latest_net_G.pth?dl=0) . Note that during the inference you need to keep only Generator weights. Put the weights into ```bash /.checkpoints/experiment_name ``` To test a model put your blurry images into a folder and run: ```bash python test.py --dataroot /.path_to_your_data --model test --dataset_mode single --learn_residual ``` ## Data Download dataset for Object Detection benchmark from [Google Drive](https://drive.google.com/file/d/1CPMBmRj-jBDO2ax4CxkBs9iczIFrs8VA/view?usp=sharing) ## Train If you want to train the model on your data run the following command to create image pairs: ```bash python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data ``` And then the following command to train the model ```bash python train.py --dataroot /.path_to_your_data --learn_residual --resize_or_crop crop --fineSize CROP_SIZE (we used 256) ``` ## Other Implementations [Keras Blog](https://blog.sicara.com/keras-generative-adversarial-networks-image-deblurring-45e3ab6977b5) [Keras Repository](https://github.com/RaphaelMeudec/deblur-gan) ## Citation If you find our code helpful in your research or work please cite our paper. ``` @article{DeblurGAN, title = {DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks}, author = {Kupyn, Orest and Budzan, Volodymyr and Mykhailych, Mykola and Mishkin, Dmytro and Matas, Jiri}, journal = {ArXiv e-prints}, eprint = {1711.07064}, year = 2017 } ``` ## Acknowledgments Code borrows heavily from [pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). The images were taken from GoPRO test dataset - [DeepDeblur](https://github.com/SeungjunNah/DeepDeblur_release)