The rapid and accurate direct multi-frame interpolation method for Digital Subtraction Angiography (DSA) images is crucial for reducing radiation and providing real-time assistance to physicians for precise diagnostics and treatment. DSA images contain complex vascular structures and various motions. Applying natural scene Video Frame Interpolation (VFI) methods results in motion artifacts, structural dissipation, and blurriness. Recently, MoSt-DSA has specifically addressed these issues for the first time and achieved SOTA results. However, MoSt-DSA's focus on real-time performance leads to insufficient suppression of high-frequency noise and incomplete filtering of low-frequency noise in the generated images. To address these issues within the same computational time scale, we propose GaraMoSt. Specifically, we optimize the network pipeline with a parallel design and propose a module named MG-MSFE. MG-MSFE extracts frame-relative motion and structural features at various granularities in a fully convolutional parallel manner and supports independent, flexible adjustment of context-aware granularity at different scales, thus enhancing computational efficiency and accuracy. Extensive experiments demonstrate that GaraMoSt achieves the SOTA performance in accuracy, robustness, visual effects, and noise suppression, comprehensively surpassing MoSt-DSA and other natural scene VFI methods. The code and models are available at https://github.com/ZyoungXu/GaraMoSt.

翻译：针对数字减影血管造影（DSA）图像的快速精准直接多帧插值方法，对于降低辐射剂量、为医师提供实时精准诊疗辅助至关重要。DSA图像包含复杂的血管结构与多样化的运动模式，直接应用自然场景视频帧插值（VFI）方法会导致运动伪影、结构耗散与模糊问题。近期提出的MoSt-DSA首次专门针对这些问题进行了研究并取得了先进成果。然而，MoSt-DSA因侧重实时性能，导致生成图像中存在高频噪声抑制不足与低频噪声滤除不彻底的问题。为在同等计算时间尺度内解决上述问题，本文提出GaraMoSt方法。具体而言，我们通过并行化设计优化了网络流程，并提出名为MG-MSFE的模块。该模块以全卷积并行方式提取多粒度帧间运动与结构特征，支持对不同尺度的上下文感知粒度进行独立灵活的调整，从而提升计算效率与精度。大量实验表明，GaraMoSt在精度、鲁棒性、视觉效果及噪声抑制方面均达到当前最优性能，全面超越了MoSt-DSA及其他自然场景VFI方法。代码与模型已公开于https://github.com/ZyoungXu/GaraMoSt。