Serial Section Microscopy Image Inpainting Guided by Axial Optical Flow

Volume electron microscopy (vEM) is becoming a prominent technique in three-dimensional (3D) cellular visualization. vEM collects a series of two-dimensional (2D) images and reconstructs ultrastructures at the nanometer scale by rational axial interpolation between neighboring sections. However, section damage inevitably occurs in the sample preparation and imaging process, suffering from manual operational errors or occasional mechanical failures. The damaged regions present blurry and contaminated structure information, even local blank holes. Despite significant progress in single-image inpainting, it is still a great challenge to recover missing biological structures, that satisfy 3D structural continuity among sections. In this paper, we propose an optical flow-based serial section inpainting architecture to effectively combine the 3D structure information from neighboring sections and 2D image features from surrounding regions. We design a two-stage reference generation strategy to predict a rational and detailed intermediate state image from coarse to fine. Then, a GAN-based inpainting network is adopted to integrate all reference information and guide the restoration of missing structures, while ensuring consistent distribution of pixel values across the 2D image. Extensive experimental results well demonstrate the superiority of our method over existing inpainting tools. Our code is available at https://github.com/chengyr1999/FlowInpaint/.