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

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Published on: April 21, 2023

Tensor Ring Based Image Enhancement.

Farnaz Sedighin¹

¹Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of Medical Signals and Sensors

|March 21, 2024

Summary

This summary is machine-generated.

This study introduces a novel Tensor Ring decomposition method for simultaneous image super-resolution and de-noising. The approach enhances low-resolution, noisy images effectively, particularly in challenging, high-noise environments.

Keywords:

Image enhancement rank incremental super-resolution tensor ring decomposition

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Area of Science:

Computer Vision
Image Processing
Multidimensional Data Analysis

Background:

Image enhancement techniques like de-noising and super-resolution are crucial across various research fields.
Traditional methods often rely on matrix or low-order analysis.
Tensor-based methods demonstrate superior performance for advanced image enhancement tasks.

Purpose of the Study:

To propose a new image super-resolution method utilizing Tensor Ring decomposition.
To address the challenge of enhancing low-resolution and noisy images.

Main Methods:

A novel image super-resolution technique based on Tensor Ring decomposition is presented.
The method extends previous tensor-based approaches by incorporating a weighted combination of images from successive stages.
This iterative approach refines image quality progressively.

Main Results:

The proposed method achieves simultaneous super-resolution and de-noising.
Simulation results validate the effectiveness of the approach, especially for images with significant noise.

Conclusions:

Tensor Ring decomposition offers a powerful framework for advanced image enhancement.
The developed method provides an effective solution for improving the quality of low-resolution and noisy images.