Papers › Spatio-Temporal Deformable Attention Network for Video Deblurring

Spatio-Temporal Deformable Attention Network for Video Deblurring

22 Jul 2022arXiv:2207.10852archive 2025-07-28

Huicong Zhang, Haozhe Xie, Hongxun Yao

The key success factor of the video deblurring methods is to compensate for the blurry pixels of the mid-frame with the sharp pixels of the adjacent video frames. Therefore, mainstream methods align the adjacent frames based on the estimated optical flows and fuse the alignment frames for restoration. However, these methods sometimes generate unsatisfactory results because they rarely consider the blur levels of pixels, which may introduce blurry pixels from video frames. Actually, not all the pixels in the video frames are sharp and beneficial for deblurring. To address this problem, we propose the spatio-temporal deformable attention network (STDANet) for video delurring, which extracts the information of sharp pixels by considering the pixel-wise blur levels of the video frames. Specifically, STDANet is an encoder-decoder network combined with the motion estimator and spatio-temporal deformable attention (STDA) module, where motion estimator predicts coarse optical flows that are used as base offsets to find the corresponding sharp pixels in STDA module. Experimental results indicate that the proposed STDANet performs favorably against state-of-the-art methods on the GoPro, DVD, and BSD datasets.

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Code

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conv huicongzhang/stdan/models/submodules.py official repository ran MIT (permissive) · 678eeae42f3728ec · report
count_parameters huicongzhang/stdan/utils/network_utils.py official repository ran MIT (permissive) · df23da82fbd7ff7b · report
get_same_padding huicongzhang/stdan/models/model/blocks.py official repository ran fingerprinted MIT (permissive) · 4ec1f46edd412686 · report
get_weight_parameters huicongzhang/stdan/utils/network_utils.py official repository ran MIT (permissive) · 00743d6b5327d649 · report
resnet_block huicongzhang/stdan/models/submodules.py official repository ran MIT (permissive) · 6831a4cc075e6346 · report
upconv huicongzhang/stdan/models/submodules.py official repository ran MIT (permissive) · 5e2116b26e2147fb · report
var_or_cuda huicongzhang/stdan/utils/network_utils.py official repository ran fingerprinted MIT (permissive) · 6b1ad2156e46dae6 · report
PSNR huicongzhang/stdan/losses/multi_loss.py official repository unverified MIT (permissive) · 933f49a6f925621f · report
l1Loss huicongzhang/stdan/losses/multi_loss.py official repository unverified MIT (permissive) · 090e64b28e61cd7f · report
load_file_list huicongzhang/stdan/datasets/make_dataset.py official repository unverified MIT (permissive) · fc6f52f692c85fca · report
load_file_list huicongzhang/stdan/datasets/make_json.py official repository unverified MIT (permissive) · 4add91bf0b53d34f · report
mseLoss huicongzhang/stdan/losses/multi_loss.py official repository unverified MIT (permissive) · c1857e1331f0f261 · report

Tasks

DeblurringDecoderVideo Deblurring

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Deblurring DVD STDAN PSNR 33.05 #6 of 7 Archive leaderboard report
Deblurring DVD STDAN SSIM 0.9374 #6 of 7 Archive leaderboard report
Deblurring GoPro STDAN PSNR 32.29 #35 of 56 Archive leaderboard report
Deblurring GoPro STDAN SSIM 0.9313 #35 of 56 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

ALIGNBASE

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