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Updated: Jan 15, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Image Super-Resolution Reconstruction Network Based on Structural Reparameterization and Feature Reuse.

Tianyu Li^1,2, Xiaoshi Jin¹, Qiang Liu²

¹School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This summary is machine-generated.

We developed efficient deep learning networks for image super-resolution reconstruction, reducing parameters by 84.5% and inference time by 49.8% for resource-constrained devices.

Keywords:

feature reuse image super-resolution reconstruction structural reparameterization

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Area of Science:

Deep learning
Computer vision
Integrated circuit engineering

Background:

Deep learning has advanced image super-resolution (SR) reconstruction for integrated circuit (IC) micrograph acquisition.
However, high memory demands of SR networks limit their deployment on resource-constrained devices.

Purpose of the Study:

To design SR networks balancing performance and complexity for efficient deployment.
To address computational redundancy in traditional SR networks.

Main Methods:

Developed SR networks using feature reuse and structural reparameterization.
Replaced redundant features with low-cost operations and designed reparameterization layers.
Created efficient deep feature extraction modules based on local feature fusion and residual learning.

Main Results:

Reduced algorithm parameters by 84.5% and inference time by 49.8% compared to performance-oriented networks.
Improved mean structural similarity index by 3.24% compared to lightweight SR algorithms.
Achieved an excellent balance between network performance and complexity.

Conclusions:

The proposed feature reuse and structural reparameterization approach enables efficient SR network deployment.
This method significantly enhances acquisition efficiency for IC micrographs in practical engineering applications.