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HSDT

Official PyTorch Implementation of Hybrid Spectral Denoising Transformer with Guided Attention. ICCV 2023

Zeqiang Lai, Chenggang Yan, Ying Fu.

🌟 Hightlights

• Superior hybrid spectral denoising transformer (HSDT), powered by
  • a novel 3D guided spectral self-attention (GSSA),
  • 3D spectral-spatial seperable convolution (S3Conv), and
  • self-modulated feed-forward network (SM-FFN).
• Super fast convergence
  • 1 epoch to reach 39.5 PSNR on ICVL Gaussian 50.
  • 3 epochs surpasses QRNN3D trained with 30 epochs.
• Super lightweight
  • HSDT-S achieves comparable performance against the SOTA with only 0.13M parameters.
  • HSDT-M outperforms the SOTA by a large margin with only 0.52M parameters.

🤗 See Also

• MAN : Another superior HSI denoising network based on RNN.
• DPHSIR : Plug-and-play ADMM that utilize HSI denoiser for unified HSI restoration without any training.
• HSIR : Out-of-box HSI denoising training, testing, and visualization framework.

Usage

Download the pretrained model at Github Release.

• Training, testing, and visualize results with HSIR.

python -m hsirun.test -a hsdt.hsdt -r ckpt/man_gaussian.pth -t icvl_512_30 icvl_512_50
python -m hsirun.train -a hsdt.hsdt -s schedule.gaussian
python -m hsirun.train -a hsdt.hsdt -s schedule.complex -r checkpoints/hsdts.hsdt/model_latest.pth
python -m hsiboard.app --logdir results

• Using our model.

import torch
from hsdt import hsdt

net = hsdt()
x = torch.randn(4,1,31,64,64)
y = net(x)

• Using our components.

import torch
from hsdt import (
    S3Conv
)

x = torch.randn(4,16,31,64,64)
block = S3Conv(16, 16, 3, 1, 1)
out = block(x) # [4,16,31,64,64]

Tips for training

• use xavier_normal_ weight initialization.

Performance

Citation

@inproceedings{lai2023hsdt,
  author = {Lai, Zeqiang and Chenggang, Yan and Fu, Ying},
  title = {Hybrid Spectral Denoising Transformer with Guided Attention},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year = {2023},
}