Papers › ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

22 May 2021arXiv:2105.10697archive 2025-07-28

Zhen Liu, Wenjie Lin, Xinpeng Li, Qing Rao, Ting Jiang, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu

In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two intractable challenges of how to handle saturation and noise properly and how to tackle misalignments caused by object motion or camera jittering. To address the former, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range (LDR) images for fusion. For the latter one, we propose to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable (PCD) alignment module. The proposed ADNet shows state-of-the-art performance compared with previous methods, achieving a PSNR-l of 39.4471 and a PSNR-μ of 37.6359 in NTIRE 2021 Multi-Frame HDR Challenge.

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Pea-Shooter/ADNet officialmentioned in papermentioned on GitHubpytorchMIT report
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ReadImages Pea-Shooter/ADNet/utils/utils.py official repository unverified MIT (permissive) · db084abce08486f8 · report
imread_uint16_png Pea-Shooter/ADNet/utils/data_io.py official repository unverified MIT (permissive) · a94d2d85a4202f16 · report
imread_uint16_png Pea-Shooter/ADNet/utils/utils.py official repository unverified MIT (permissive) · 1ef20e26f4e1eae2 · report
imwrite_uint16_png Pea-Shooter/ADNet/utils/data_io.py official repository unverified MIT (permissive) · 2b9ad21f01f90209 · report
imwrite_uint16_png Pea-Shooter/ADNet/utils/utils.py official repository unverified MIT (permissive) · 812129ac69789e90 · report
mu_tonemap Pea-Shooter/ADNet/graphs/loss/muloss.py official repository unverified MIT (permissive) · 87976644789c2696 · report
mu_tonemap Pea-Shooter/ADNet/utils/metrics.py official repository unverified MIT (permissive) · 6116c9f649617da4 · report
norm_mu_tonemap Pea-Shooter/ADNet/utils/metrics.py official repository unverified MIT (permissive) · f7c87bc58708bfbf · report
tanh_norm_mu_tonemap Pea-Shooter/ADNet/graphs/loss/muloss.py official repository unverified MIT (permissive) · eb09a1b5639e5ba3 · report
tanh_norm_mu_tonemap Pea-Shooter/ADNet/utils/metrics.py official repository unverified MIT (permissive) · cb93029ff87f983e · report

Tasks

Face AlignmentVocal Bursts Intensity Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Face Alignment WFW (Extra Data) ADNet AUC@10 (inter-ocular) 60.22 #5 of 11 Archive leaderboard report
Face Alignment WFW (Extra Data) ADNet FR@10 (inter-ocular) 2.72 #5 of 11 Archive leaderboard report
Face Alignment WFW (Extra Data) ADNet NME (inter-ocular) 4.14 #5 of 11 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

Average PoolingConvolutionMax PoolingSigmoid Activation

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