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, vol. 46, no. 12, pp. 9818-9833, 2024
https://doi.org/10.1109/TPAMI.2024.3429498

For PDF, please visit https://mtntruong.github.io/
The appendix, which is somehow not included in the publisher's version, is freely available on the above website or can be directly accessed at this link.

If you have any questions, please open an issue.
The algorithm can also be applied to other applications. Please feel free to ask if you need help training the algorithm on other datasets.

The proposed algorithm is implemented in Python using PyTorch 1.11. There are two subfolders in this repository:

• AGTC-HDR: application of the proposed algorithm in multi-exposure fusion-based HDR imaging (Section 4.1 in the paper)
• AGTC-HSI: application in hyperspectral image restoration (Section 4.2 in the paper)

Since the input of the proposed deep network is as simple as

data   = torch.rand(1, 103, 64, 64).cuda()
omega  = (torch.rand(1, 103, 64, 64) < 0.9).float().cuda()
model  = RPCA_Net(N_iter=10, tensor_num_channels=103).cuda()
output = model(data, omega)

you can easily plug this model into your training codes. Important notes:

• The batch size must be 1.
• The variable omega is binary, since it's a mask indicating observed entries.

Please use env.yml to create an environment with 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 README.md file in each subfolder for detailed instructions on downloading and preprocessing data, running scripts, and using pre-trained weights for the corresponding task.

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},
  volume={46},
  number={12},
  pages={9818-9833},
  doi={10.1109/TPAMI.2024.3429498}
}