Transformer Perceptual Loss for Image Deblurring

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This repository is an official implementation of Image Deblurring by Exploring In-depth Properties of Transformer.

Basic usage

from loss.deblur_loss import ReconstructPerceptualLoss as ReconstructLoss



model = yourmodel()
criterion = ReconstructLoss(opt)
model = model.cuda()
criterion.pretrain_mae = criterion.pretrain_mae.to(torch.device('cuda'))

for index, train_data in tqdm(enumerate(train_loader)):
    gt, b_img = train_data
    b_img = b_img.cuda()
    gt_img = gt.cuda()
    x = b_img
    recover_img = model(x)
    losses = criterion(recover_img, gt_img)
    grad_loss = losses["total_loss"]
    optimizer.zero_grad()
    grad_loss.backward()
    optimizer.step()

If this repo help you, please cite us:

@article{liang2024image,
  title={Image deblurring by exploring in-depth properties of transformer},
  author={Liang, Pengwei and Jiang, Junjun and Liu, Xianming and Ma, Jiayi},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2024},
  publisher={IEEE}
}