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rajeevyasarla
2019-08-06 14:21:17 -04:00
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@@ -14,14 +14,14 @@ We propose a novel multi-stream architecture and training methodology that explo
4. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0) 4. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)
## To test UMRL: ## To test UMSN:
1. Download test datasets provided the authors of Ziyi et al. 1. Download test datasets provided the authors of Ziyi et al.
- https://sites.google.com/site/ziyishenmi/cvpr18_face_deblur - https://sites.google.com/site/ziyishenmi/cvpr18_face_deblur
2. run test_data_generation.m 2. run test_data_generation.m
- It renames the files counting from 1, for example 000001.png - It renames the files counting from 1, for example 000001.png
3. python test_face_deblur.py --dataroot ./facades/github/ --valDataroot <path_to_test_data> --netG ./pretrained_models/Deblur_epoch_Best.pth 3. python test_face_deblur.py --dataroot ./facades/github/ --valDataroot <path_to_test_data> --netG ./pretrained_models/Deblur_epoch_Best.pth
## To train UMRL: ## To train UMSN:
1. Kernels are generated using, 1. Kernels are generated using,
- [Boracchi and Foi, 2012] Modeling the Performance of Image Restoration from Motion Blur Giacomo Boracchi and Alessandro Foi, Image Processing, IEEE Transactions on. vol.21, no.8, pp. 3502 - 3517, Aug. 2012, - [Boracchi and Foi, 2012] Modeling the Performance of Image Restoration from Motion Blur Giacomo Boracchi and Alessandro Foi, Image Processing, IEEE Transactions on. vol.21, no.8, pp. 3502 - 3517, Aug. 2012,
- 25000 kernels with size ranging from 13 to 29 are generated and saved as ".mat" file - 25000 kernels with size ranging from 13 to 29 are generated and saved as ".mat" file