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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Local feature enhancement transformer for image super-resolution.

Huang Weijie¹, Huang Detian²

¹School of Business, Huaqiao University, Quanzhou, 362021, Fujian Province, China.

Scientific Reports

|July 2, 2025

Summary

This summary is machine-generated.

We introduce a local feature enhancement transformer (LFESR) for image super-resolution (SR). LFESR improves detail reconstruction by balancing global and local feature learning, outperforming existing methods.

Keywords:

Attention mechanism Global context information Image super-resolution Local feature interaction Transformer

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Transformer models excel in image super-resolution (SR) due to their ability to model long-range dependencies.
However, increasing window sizes in transformer-based SR models can degrade the learning of fine local features, leading to blurry reconstructions.

Purpose of the Study:

To propose a novel Local Feature Enhancement Transformer for Image Super-Resolution (LFESR) that enhances local feature interaction while retaining global context.
To address the limitations of existing transformer-based SR methods in capturing fine-level details.

Main Methods:

Developed the Local Feature Enhancement Transformer (LFET) module, balancing spatial processing and channel configuration in self-attention.
Incorporated Neighborhood Self-Attention (NSA) using Hadamard operations for enhanced local interaction and a novel ghost head to increase channel capacity.
Integrated ConvFFN to further strengthen high-frequency details in reconstructed images.

Main Results:

LFESR significantly outperformed state-of-the-art methods in both visual quality and quantitative metrics.
Achieved superior performance compared to SwinIR, notably exceeding it by 0.49 dB and 0.52 dB in PSNR at a 4x scaling factor on Urban100 and Manga109 datasets, respectively.

Conclusions:

The proposed LFESR effectively enhances local feature interaction, leading to high-quality image super-resolution.
LFESR demonstrates superior performance, particularly in reconstructing fine details, making it a promising advancement in image SR technology.