Papers › Spatio-Temporal Filter Adaptive Network for Video Deblurring

Spatio-Temporal Filter Adaptive Network for Video Deblurring

28 Apr 2019ICCV 2019 10arXiv:1904.12257archive 2025-07-28

Shangchen Zhou, Jiawei Zhang, Jinshan Pan, Haozhe Xie, WangMeng Zuo, Jimmy Ren

Video deblurring is a challenging task due to the spatially variant blur caused by camera shake, object motions, and depth variations, etc. Existing methods usually estimate optical flow in the blurry video to align consecutive frames or approximate blur kernels. However, they tend to generate artifacts or cannot effectively remove blur when the estimated optical flow is not accurate. To overcome the limitation of separate optical flow estimation, we propose a Spatio-Temporal Filter Adaptive Network (STFAN) for the alignment and deblurring in a unified framework. The proposed STFAN takes both blurry and restored images of the previous frame as well as blurry image of the current frame as input, and dynamically generates the spatially adaptive filters for the alignment and deblurring. We then propose the new Filter Adaptive Convolutional (FAC) layer to align the deblurred features of the previous frame with the current frame and remove the spatially variant blur from the features of the current frame. Finally, we develop a reconstruction network which takes the fusion of two transformed features to restore the clear frames. Both quantitative and qualitative evaluation results on the benchmark datasets and real-world videos demonstrate that the proposed algorithm performs favorably against state-of-the-art methods in terms of accuracy, speed as well as model size.

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sczhou/STFAN mentioned on GitHubpytorchMIT report

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PSNR sczhou/STFAN/losses/multiscaleloss.py community (archive-listed) ran · violated contract fingerprinted MIT (permissive) · 1a63d78c6d1a0024 · report
count_parameters sczhou/STFAN/utils/network_utils.py community (archive-listed) ran MIT (permissive) · df23da82fbd7ff7b · report
get_weight_parameters sczhou/STFAN/utils/network_utils.py community (archive-listed) ran MIT (permissive) · 00743d6b5327d649 · report
var_or_cuda sczhou/STFAN/utils/network_utils.py community (archive-listed) ran fingerprinted MIT (permissive) · 6b1ad2156e46dae6 · report
mseLoss sczhou/STFAN/losses/multiscaleloss.py community (archive-listed) unverified MIT (permissive) · 3d11904eb8112e2d · report
perceptualLoss sczhou/STFAN/losses/multiscaleloss.py community (archive-listed) unverified MIT (permissive) · 80766eb7a915cf75 · report
readPFM sczhou/STFAN/utils/imgio_gen.py community (archive-listed) unverified MIT (permissive) · 0564ba38597daf7c · report

Tasks

DeblurringImage DeblurringOptical Flow EstimationVideo Deblurring

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Deblurring DVD STFAN PSNR 31.27 #3 of 3 Archive leaderboard report
Deblurring GoPro STFAN PSNR 28.59 #53 of 56 Archive leaderboard report
Deblurring GoPro STFAN SSIM 0.861 #53 of 56 Archive leaderboard report
Image Deblurring GoPro STFAN PSNR 28.59 #51 of 55 Archive leaderboard report
Image Deblurring GoPro STFAN SSIM 0.861 #51 of 55 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

SPEED

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