High dynamic range imaging aims to recover the luminance and color information of real-world scenes, thereby overcoming the common problems of highlight saturation and shadow detail loss in conventional sensor imaging. It has now been extended to applications such as autonomous driving and virtual reality/augmented reality. However, artifact removal in dynamic scenes remains a central challenge. To address this issue, this paper systematically reviewed the related datasets and evaluation metrics, comprehensively summarized the major research advances, and further analyzed the inherent causes of imaging model deficiencies and the current technical bottlenecks. It also compared and analyzed the performance differences among existing state-of-the-art methods from the perspectives of model generalization ability, computational complexity, and inference time. Building on recent development trends, the paper further identified three levels of important research directions, namely fundamental core challenges, key performance optimization, and frontier technology exploration, with the aim of providing a useful reference for both academic research and engineering practice.