  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-$\mu$ of 37.6359 in NTIRE 2021 Multi-Frame HDR
Challenge.

Computer Vision and Pattern Recognition