UMSN-Face-Deblurring

Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks

Rajeev Yasarla, Federico Perazzi, Vishal M. Patel

Paper Link

We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided MultiStream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards challenging regions of the human face such as the eyes and nose. The entire network is trained in an endto-end fashion.

Prerequisites:

1. Linux
2. Python 2 or 3
3. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)

To test UMRL:

1. Download test datasets provided the authors of Ziyi et al.
   • https://sites.google.com/site/ziyishenmi/cvpr18_face_deblur
2. run test_data_generation.m
   • It renames the files counting from example 000001.png
3. python test_face_deblur.py --dataroot ./facades/github/ --valDataroot <path_to_test_data> --netG ./pretrained_models/Deblur_epoch_Best.pth

To train UMRL:

python train_face_deblur.py --dataroot <path_to_train_data> --valDataroot ./facades/github/ --exp ./face_deblur --batchSize 10

• input should be clean image. blurry images for training are generated by the code it self.