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.

本研究提出一种基于注意力引导的变形卷积神经网络 ADNet，用于携带多帧高动态范围（HDR）成像，解决曝光过度、噪点和物体运动或摄像头抖动导致的图像不对准等问题，ADNet 在与先前方法的比较中表现出最先进的性能，在 NTIRE 2021 多帧 HDR 挑战中实现了 39.4471 的 PSNR-l 和 37.6359 的 PSNR-μ。