Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results when motion and saturation occur. To generate visually pleasing HDR images in various cases, we propose a hybrid HDR deghosting network, called HyHDRNet, to learn the complicated relationship between reference and non-reference images. The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork. Specifically, to effectively avoid ghosting from the source, the content alignment subnetwork uses patch aggregation and ghost attention to integrate similar content from other non-reference images with patch level and suppress undesired components with pixel level. To achieve mutual guidance between patch-level and pixel-level, we leverage a gating module to sufficiently swap useful information both in ghosted and saturated regions. Furthermore, to obtain a high-quality HDR image, the Transformer-based fusion subnetwork uses a Residual Deformable Transformer Block (RDTB) to adaptively merge information for different exposed regions. We examined the proposed method on four widely used public HDR image deghosting datasets. Experiments demonstrate that HyHDRNet outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.

翻译：将不同曝光度的低动态范围图像映射为高动态范围图像，在动态场景中仍具挑战性，因为物体运动或相机抖动会产生鬼影。随着深度神经网络的成功应用，基于深度神经网络的鬼影抑制方法虽被广泛提出，但在运动与饱和共存场景中难以产生令人满意的结果。为在不同场景下生成视觉美观的HDR图像，本文提出一种混合HDR去鬼影网络HyHDRNet，用于学习参考图像与非参考图像间的复杂关系。该网络由内容对齐子网络与基于Transformer的融合子网络构成。具体而言，内容对齐子网络通过块聚合与鬼影注意力机制，从源图像中有效消除鬼影：以块级方式整合非参考图像中的相似内容，以像素级方式抑制非理想成分。通过门控模块实现块级与像素级信息的相互引导，在鬼影区域与饱和区域充分交换有效信息。此外，为获得高质量HDR图像，基于Transformer的融合子网络采用残差可变形Transformer块自适应融合不同曝光区域的信息。在四个广泛使用的公开HDR图像去鬼影数据集上的实验表明，HyHDRNet在定量与定性指标上均优于当前最优方法，能生成纹理与色彩统一的优异HDR可视化结果。