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.
翻译:在本文中,我们为手持多框架动态高射程成像(HDR)提供了一个引人注意的变形变形网络,即ADNet, 这一问题包括两个棘手的挑战,即如何正确处理饱和和和噪音,以及如何处理物体运动或照相机震动造成的不匹配问题,为了应对前者,我们采用了一个空间注意模块,以适应方式选择不同暴露低动态范围图像的最适当区域进行聚合。对于后者,我们提议将特征级别的伽玛校正图像与Pyramid、累加和变形(PCD)校准模块相匹配。与以往方法相比,拟议的ADNet显示最先进的性能,实现了39.4471美元的PSNR-l和2021年多森林人类发展报告挑战中37.6359美元的PSNR-NR-mu$。