Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea.
Samsung Electronics, Suwon 16677, Korea.
Sensors (Basel). 2020 Sep 7;20(18):5102. doi: 10.3390/s20185102.
This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting artifacts in the presence of large object motion. Flow-based methods face the same issue since their optical flow algorithms yield huge alignment errors. To eliminate ghosting artifacts, we propose a simple yet effective alignment network for solving the misalignment. The proposed pyramid inter-attention module (PIAM) performs alignment of LDR features by leveraging inter-attention maps. Additionally, to boost the representation of aligned features in the merging process, we propose a dual excitation block (DEB) that recalibrates each feature both spatially and channel-wise. Exhaustive experimental results demonstrate the effectiveness of the proposed PIAM and DEB, achieving state-of-the-art performance in terms of producing ghost-free HDR images.
本文提出了一种新的方法，用于对动态场景进行高动态范围（HDR）成像，以消除在输入低动态范围（LDR）图像中存在严重失准（大物体或相机运动）时 HDR 图像中的重影伪影。最近的非流方法在存在大物体运动时会出现重影伪影。基于流的方法也存在同样的问题，因为它们的光流算法会产生很大的对准误差。为了消除重影伪影，我们提出了一种简单而有效的对齐网络来解决失准问题。所提出的金字塔互注意力模块（PIAM）通过利用互注意力图来实现 LDR 特征的对齐。此外，为了在合并过程中增强对齐特征的表示，我们提出了一种双重激励块（DEB），它在空间和通道两个维度上重新校准每个特征。详尽的实验结果表明，所提出的 PIAM 和 DEB 是有效的，在生成无重影的 HDR 图像方面达到了最先进的性能。
