多帧恢复用于高帧率利萨如共聚焦激光内窥镜
Multi-frame Restoration for 10 Hz Lissajous Confocal Laser Endomicroscopy
AI总结：
本文提出MIRA框架，通过特征重用和位移对齐实现多帧利萨如共聚焦激光内窥镜图像恢复，提升恢复质量和计算效率。
摘要：
利萨如共聚焦激光内窥镜（CLE）是一种有前景的高速活体光学活检解决方案，适用于手持场景。然而，利萨如扫描轨迹呈共振轨迹，每帧仅采样访问过的像素；在高帧率下，许多像素未被访问，产生结构化孔洞。本文介绍了首个高帧率利萨如CLE基准，包含低质量视频片段与高质量参考图像。参考图像通过拼接稳定、慢扫描帧获得，实现时间对齐监督。基于此数据集，我们提出MIRA，一种轻量级递归框架，通过特征重用和位移对齐迭代聚合时间上下文。实验表明，MIRA在恢复质量上优于轻量级和高复杂度基线，同时保持良好的计算效率，适合临床部署。
英文摘要：
Lissajous confocal laser endomicroscopy (CLE) is a promising solution for high-speed in vivo optical biopsy for handheld scenarios. However, Lissajous scanning traces a resonant trajectory and samples only the visited pixels per frame; at high frame rates, many pixels remain unvisited, creating structured holes. In this work, we introduce the first benchmark for 10 Hz Lissajous CLE, consisting of low-quality video clips paired with high-quality reference images. The reference images are wide-FOV mosaics obtained by stitching stabilized, slow-scan frames of the same tissue, enabling temporally aligned supervision. Using this dataset, we propose MIRA, a lightweight recurrent framework for Lissajous CLE restoration that iteratively aggregates temporal context through feature reuse and displacement alignment. Our experiments demonstrate that MIRA outperforms both lightweight and high-complexity baselines in restoration quality while maintaining a favorable computational efficiency suitable for clinical deployment.
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