Merging multi-exposure images is a common approach for obtaining high dynamic range (HDR) images, with the primary challenge being the avoidance of ghosting artifacts in dynamic scenes. Recent methods have proposed using deep neural networks for deghosting. However, the methods typically rely on sufficient data with HDR ground-truths, which are difficult and costly to collect. In this work, to eliminate the need for labeled data, we propose SelfHDR, a self-supervised HDR reconstruction method that only requires dynamic multi-exposure images during training. Specifically, SelfHDR learns a reconstruction network under the supervision of two complementary components, which can be constructed from multi-exposure images and focus on HDR color as well as structure, respectively. The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image. During testing, the learned reconstruction network is directly deployed to predict an HDR image. Experiments on real-world images demonstrate our SelfHDR achieves superior results against the state-of-the-art self-supervised methods, and comparable performance to supervised ones. Codes are available at https://github.com/cszhilu1998/SelfHDR

翻译：融合多曝光图像是获取高动态范围（HDR）图像的常用方法，其主要挑战在于避免动态场景中的鬼影伪影。现有方法已提出利用深度神经网络进行去鬼影处理，然而这些方法通常依赖包含HDR真值的充足数据，而此类数据的采集既困难又成本高昂。为消除对标注数据的需求，本文提出SelfHDR——一种仅需训练时输入动态多曝光图像的自监督HDR重建方法。具体而言，SelfHDR通过两个互补监督组件学习重建网络：这两个组件可由多曝光图像构建，分别聚焦于HDR色彩和结构信息。色彩分量通过对齐后的多曝光图像估计得到，而结构分量则通过一个以色彩分量和输入参考图像（如中等曝光图像）为监督的结构聚焦网络生成。测试阶段，直接部署训练好的重建网络预测HDR图像。真实场景图像实验表明，SelfHDR在性能上优于现有最优自监督方法，且与监督方法表现相当。代码已开源至https://github.com/cszhilu1998/SelfHDR。