DeepDeblur: Text Image Recovery from Blur to Sharp
This work focuses on recovering the blurry text image.
Based on the deep neural network, a new short connection scheme is used. Trained by the pixel regression, higher visual quality of the image can be recovered by the network from the blurry one.
Sequential Highway Connection (SHC) structure
Loss curve comparing with the ResNet Structure:
Jianhan Mei at Nanyang Technological University
Xiang Chen at Darmstadt University of Technology
Ziming Wu at The Hong Kong University of Science and Technology
Python 2/3, in case you need the sufficient scientific computing packages, we recommend you to install anaconda.
Tensorflow >= 1.5.0
Keras >= 2.2.0
Optional: if you need GPUs acceleration, please install CUDA that the version requires >= 9.0
Running The Demo
Text Image Dataset Generation
Check the matlab script 'Matlab/RunProcess.m': The path of the text images should contain raw sharp text images. You can build your own dataset by convert PDF files into raw image files and save them to the text image path in "Matlab/RunProcess.m".
Then run the matlab script 'Matlab/RunProcess.m', which helps to build the training dataset.
Check the training data and model saving paths in "train.py", for which the training data should be consistent with the previous step. Then run the following script:
Check the testing model and data paths in "test.py". Then run the following script:
This work is released under the MIT License (refer to the LICENSE file for details).