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Super-resolution Imaging of Neuronal Dense-core Vesicles

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Published on: July 2, 2014

Implicit neural network-based coal SEM super-resolution for enhancing micro-pores measurement tasks.

Xiaowei An¹, Shenghua Teng², Zhuopeng Wang²

¹College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266510, Shandong, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 17, 2026

Summary

This study introduces a new super-resolution (SR) framework using implicit neural representation and Half-Quadratic Splitting optimization to reduce radiation damage in Scanning Electron Microscopy (SEM). The method enhances image quality and ensures stability for precise measurements.

Keywords:

Coal micro-pores measurement Implicit neural representation Interactive-interpretable Super resolution

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Area of Science:

Materials Science
Microscopy
Computational Imaging

Background:

Scanning Electron Microscopy (SEM) requires high resolution for detailed analysis.
Prolonged radiation exposure during SEM can cause structural damage to specimens.
Existing super-resolution (SR) methods may not adequately address radiation-induced artifacts or ensure measurement stability.

Purpose of the Study:

To develop an interactive-interpretable super-resolution (SR) framework to mitigate radiation damage in SEM.
To improve visual fidelity and geometric accuracy of high-resolution SEM images.
To provide a stable and applicable solution for geometry-sensitive measurement tasks.

Main Methods:

Integration of implicit neural representation (INR) with model-driven Half-Quadratic Splitting (HQS) optimization.
Utilizing a local window attention mechanism within INR for contextual dependencies.
Employing an interactive dual-branch network to decouple feature content and positional encoding.
Unfolding the HQS algorithm into a deep, interpretable network for transparent optimization steps.

Main Results:

The proposed SR framework significantly outperforms state-of-the-art SR algorithms in visual fidelity.
Demonstrated applicability and stability in downstream geometry-sensitive measurement tasks.
The method effectively addresses structural damage risks associated with prolonged radiation exposure in SEM.

Conclusions:

The interactive-interpretable SR framework offers a robust solution for high-resolution SEM imaging under radiation.
The integration of INR and HQS optimization enhances image quality while preserving geometric integrity.
This approach is valuable for applications requiring precise measurements from radiation-exposed specimens.