In this paper, we propose LC-Mamba, a Mamba-basedmodel that captures fine-grained spatiotemporal infor-mation in video frames, addressing limitations in cur-rent interpolation methods and enhancing performance.The main contributions are as follows: First, we apply ashifted local window technique to reduce historical de-cay and enhance local spatial features, allowing multi-scale capture of detailed motion between frames. Sec-ond, we introduce a Hilbert curve-based selective statescan to maintain continuity across window boundaries,preserving spatial correlations both within and betweenwindows. Third, we extend the Hilbert curve to enablevoxel-level scanning to effectively capture spatiotempo-ral characteristics between frames. The proposed LC-Mamba achieves competitive results, with a PSNR of36.53 dB on Vimeo-90k, outperforming prior models by+0.03 dB. The code and models are publicly available athttps://anonymous.4open.science/r/LC-Mamba-FE7C