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（LDR）图像映射为高动态范围（HDR）图像，在处理动态场景时仍面临挑战，这是由于物体运动或相机抖动导致的鬼影问题。尽管深度神经网络（DNN）的成功推动了多种基于DNN的去鬼影方法，但当运动与饱和区域共存时，这些方法难以生成令人满意的结果。为在各种场景下生成视觉舒适的HDR图像，我们提出了一种混合型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联。该网络由内容对齐子网络和基于Transformer的融合子网络构成。具体而言，为从源头有效消除鬼影，内容对齐子网络采用块聚合与鬼影注意力机制，一方面通过块级操作整合其他非参考图像中的相似内容，另一方面基于像素级机制抑制不期望的成分。为实现块级与像素级特征的相互引导，我们引入门控模块，在鬼影区域和饱和区域中充分交换有用信息。此外，为获取高质量HDR图像，基于Transformer的融合子网络采用残差可变形Transformer块（RDTB），自适应融合不同曝光区域的信息。我们在四个广泛使用的公开HDR图像去鬼影数据集上验证了所提方法。实验结果表明，HyHDRNet在定量和定性评估上均优于现有最优方法，能够生成具有统一纹理与色彩的理想HDR可视化结果。