Abstract
High-resolution (HR) medical videos are vital for accurate diagnosis, yet are hard to acquire due to hardware limitations and physiological constraints. Clinically, the collected low-resolution (LR) medical videos present unique challenges for video super-resolution (VSR) models, including camera shake, noise, and abrupt frame transitions, which result in significant optical flow errors and alignment difficulties. Additionally, tissues and organs exhibit continuous and nuanced structures, but current VSR models are prone to introducing artifacts and distorted features that can mislead doctors. To this end, we propose MedVSR, a tailored framework for medical VSR. It first employs Cross State-Space Propagation (CSSP) to address the imprecise alignment by projecting distant frames as control matrices within state-space models, enabling the selective propagation of consistent and informative features to neighboring frames for effective alignment. Moreover, we design an Inner State-Space Reconstruction (ISSR) module that enhances tissue structures and reduces artifacts with joint long-range spatial feature learning and large-kernel short-range information aggregation. Experiments across four datasets in diverse medical scenarios, including endoscopy and cataract surgeries, show that MedVSR significantly outperforms existing VSR models in reconstruction performance and efficiency. Code released at this https URL.
Abstract (translated)
高分辨率（HR）医学视频对于准确诊断至关重要，但由于硬件限制和生理约束，很难获取。在临床上，收集到的低分辨率（LR）医学视频为视频超分辨率（VSR）模型带来了独特的挑战，包括摄像机抖动、噪声及画面间的突然过渡，这些问题会导致显著的光流误差和对齐困难。此外，组织和器官展示出持续且细微的结构特征，但目前的VSR模型容易引入伪影和扭曲特性，这些可能误导医生的判断。为此，我们提出了MedVSR，这是一个专门用于医学视频超分辨率（VSR）的框架。 MedVSR首先采用交叉状态空间传播（CSSP），通过在状态空间模型中将相隔较远的画面投影为控制矩阵来解决不精确对齐的问题。这使得可以有选择地向相邻帧传递一致且具有信息量的特征，以实现有效的画面对齐。此外，我们设计了一个内态空间重建模块（ISSR），它通过联合学习长程空间特征和大核短程信息聚合，增强组织结构并减少伪影。 在包括内窥镜检查和白内障手术在内的四个不同医学场景的数据集上进行的实验表明，MedVSR在重构性能和效率方面显著优于现有的VSR模型。代码可在上述链接中获取。
URL
https://arxiv.org/abs/2509.21265
