The dataset viewer is not available because its heuristics could not detect any supported data files. You can try uploading some data files, or configuring the data files location manually.
Github: https://github.com/kqwang/Phase_unwrapping_by_U-Net
Deep learning phase unwrapping with a U-Net
A demonstrate of "Deep learning spatial phase unwrapping: a comparative review" and "One-step robust deep learning phase unwrapping".
Preparation
Install dependencies
pip install -r requirements.txt
Datasets
Download dataset (without noise) from here to the current path and unzip. The file structure is the following:
train_in
└── 000001.mat
...
└── 020000.mat
train_gt
└── 000001.mat
...
└── 020000.mat
test_in
└── 000001.mat
...
└── 002000.mat
test_gt
└── 000001.mat
...
└── 000421.mat
test_in_real
└── 000001.mat
...
└── 000421.mat
test_gt_real
└── 000001.mat
...
└── 000421.mat
Of course, datasets train_in, train_gt, test_in and test_gt can also be obtained by running dataset_generation.m with MATLAB , whose parameters can be adjusted according to actual needs. (The size range of the noise can be controlled by adjusting the parameter noise_max.)
Network traning
Run main_train.py to start training the neural network.
python main_train.py
After training, two files (loss and others.csv and weights.pth) will be saved in the folder model_weights
Network testing
Run main_test.py to do some test.
python main_test.py
Error statistics
Run error_evaluation.m with MATLAB to calculate RMSEs for each test result.
More informarion
Details about the code and dataset can be found in this paper and its Supplementary Materials.
- Downloads last month
- 67