ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
TL
TL;DR
This paper presents an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet, and proposes to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable alignment module.
Abstract
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 expo-sure 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 AD-Net 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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UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
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Understanding Deformable Alignment in Video Super-Resolution
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