Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images

TL;DR

This paper introduces a novel coarse-to-fine interpolation framework for electron microscopic images, utilizing temporal spatial-adaptive interpolation and deformable refinement to improve quality and handle deformation issues.

Contribution

The proposed method combines TSA interpolation and SDRB to enhance electron microscopic image interpolation, addressing limitations of flow-based methods.

Findings

Outperforms previous methods in PSNR and qualitative assessments

Effectively handles unstable quality and deformation in EM images

Provides superior reconstruction accuracy

Abstract

Recently, flow-based methods have achieved promising success in video frame interpolation. However, electron microscopic (EM) images suffer from unstable image quality, low PSNR, and disorderly deformation. Existing flow-based interpolation methods cannot precisely compute optical flow for EM images since only predicting each position's unique offset. To overcome these problems, we propose a novel interpolation framework for EM images that progressively synthesizes interpolated features in a coarse-to-fine manner. First, we extract missing intermediate features by the proposed temporal spatial-adaptive (TSA) interpolation module. The TSA interpolation module aggregates temporal contexts and then adaptively samples the spatial-related features with the proposed residual spatial adaptive block. Second, we introduce a stacked deformable refinement block (SDRB) further enhance the reconstruction quality, which is aware of the matching positions and relevant features from input frames with the feedback mechanism. Experimental results demonstrate the superior performance of our approach compared to previous works, both quantitatively and qualitatively.