Source code and data for the paper
Attention-Guided Low-Rank Tensor Completion
Truong Thanh Nhat Mai, Edmund Y. Lam, and Chul Lee
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
https://doi.org/10.1109/TPAMI.2024.3429498
For PDF, please visit https://mtntruong.github.io/
If you have any question, please open an issue.
The algorithm can also be applied to other applications. Please feel free to ask if you need help with training the algorithm using other datasets.
The proposed algorithm is implemented in Python using PyTorch 1.11.
We first upload the source codes of the proposed algorithm. Data and pre-trained weights will be updated later. Since the inputs of the proposed algorithm is as simple as
data = torch.rand(1, 103, 64, 64)
omega = torch.rand(1, 103, 64, 64) < 0.9
model = RPCA_Net(N_iter=10)
output = model(data, omega)
you can easily plug this model into your training codes. N. B. The batch size must be 1, omega is binary, and the number of channels (103 in this example) is hard-coded in main_net.py.
Please also note that the source codes have not been refactored yet, so they are a little ugly.
I will try to improve the readability and quality of this repository over time. I have been a bit busy recently due to company work.
The training/testing scripts of AGTC is similar to those of LRT-HDR. You may have a look at them in the meantime.
Please use env.yml to create an environment in Anaconda
conda env create -f env.yml
Then activate the environment
conda activate agtc
If you want to change the environment name, edit the first line of env.yml before creating the environment.
Please see the scripts in Data-Preparation folder in each task. Note that the data for HDR imaging have not been uploaded yet.
To be updated
If our algorithm is useful for your research, please kindly cite our work
@ARTICLE{Mai2024,
author={Mai, Truong Thanh Nhat and Lam, Edmund Y. and Lee, Chul},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Attention-Guided Low-Rank Tensor Completion},
year={2024},
pages={1-17},
doi={10.1109/TPAMI.2024.3429498}}
}