解决10Hz扫描内窥镜图像的结构化缺损问题，实现高效清晰重建。
Multi-frame Restoration for 10 Hz Lissajous Confocal Laser Endomicroscopy
- 基于帧间特征复用与位移对齐，迭代聚合时间上下文信息。
- 在10Hz Lissajous CLE数据集上超越轻量与复杂基线模型。
- 轻量设计适合临床实时部署，为手持内窥提供高质量影像支持。
Lissajous共焦激光内窥镜（CLE）是一种适用于手持场景的高速在体光学活检方案。然而，其谐振扫描轨迹仅采样每帧访问的像素，在高帧率下大量像素未被覆盖，导致结构化空洞。本文首次构建了10 Hz Lissajous CLE基准数据集，包含低质量视频片段与高质量参考图像；参考图像是通过拼接同一组织的稳定慢扫帧生成的宽视场全景图，实现时间对齐监督。基于该数据集，我们提出MIRA——一种轻量级递归框架，通过特征复用和位移对齐迭代聚合时间上下文。实验表明，MIRA在重建质量上优于轻量与高复杂度基线模型，同时保持适合临床部署的计算效率。
原文摘要 · Abstract (English)
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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