MOTIVATION: Serial section electron microscopy (ssEM) is essential for studying biological cell structures at nanometer resolution. However, supporting film folding (SFF) degradation frequently occurs during sample preparation, causing structural distortions and information loss that severely impair downstream analyses such as 3D reconstruction and neuron segmentation. RESULTS: We propose RegInpaint, a novel recovery framework that jointly addresses deformation correction and missing-information restoration caused by SFF degradation. RegInpaint formulates SFF recovery as a joint problem of 3D elastic registration and image inpainting, providing a generalizable solution for ssEM restoration. Experiments on four EM datasets show that RegInpaint consistently outperforms existing methods in image restoration quality and significantly improves neuron segmentation accuracy. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/zhangzhenbang2021/RegInpaint.git.

Series Section Electron Microscopy (ssEM) has emerged as a pivotal technology for deciphering nanoscale biological architectures. Three-dimensional (3D) registration is a critical step in ssEM, tasked with rectifying axial misalignments and nonlinear distortions introduced during serial sectioning. The core scientific challenge lies in achieving distortion mitigation without erasing the natural morphological deformations of biological tissues, thereby enabling faithful reconstruction of 3D ultrastructural organization. In this study, we present a paradigm-shifting optimization framework that rethinks 3D registration through the lens of manifold trajectory optimization. We propose the first continuous trajectory dynamics formulation for 3D registration and introduce a novel optimization strategy. Specifically, we introduce a dual optimization objective that inherently balances global trajectory smoothness with local structural preservation, while developing a solver that combines Gauss-Seidel iteration with Neural ODEs to systematically integrate biophysical priors with data-driven deformation compensation. A key strength of our method is its fully unsupervised training, which avoids reliance on ground truth and suits ssEM scenarios where annotations are difficult to obtain. Extensive experiments on multiple datasets spanning diverse tissue types demonstrate our method's superior performance in structural restoration accuracy and cross-tissue robustness.