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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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Published on: August 23, 2017

Robust Multi-Frame Super-Resolution Based on Adaptive Half-Quadratic Function and Local Structure Tensor Weighted

Shanshan Liu^1,2, Minghui Wang¹, Qingbin Huang³

¹College of Computer Science, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

This summary is machine-generated.

This study introduces a novel multi-frame super-resolution method using minimum variance image selection and advanced prior knowledge. The technique effectively enhances image resolution, preserving details and reducing noise for superior visual quality.

Keywords:

bilateral total variation half-quadratic function local structure tensor multi-frame super-resolution preserve the edge information

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Hardware-based image resolution improvements face technological and cost limitations.
High-resolution images are crucial for numerous application fields.
Existing multi-frame super-resolution methods struggle with preserving edge/texture details and noise suppression.

Purpose of the Study:

To develop an effective multi-frame super-resolution technique addressing limitations of current methods.
To improve the preservation of image edge and texture details.
To efficiently remove noise while enhancing image resolution.

Main Methods:

A minimum variance method for selecting high-quality low-resolution images.
Utilizing a half-quadratic function as a loss function to minimize observation errors.
Employing a combination of local structure tensor and Bilateral Total Variation (BTV) as image prior knowledge.

Main Results:

The proposed method demonstrates superior preservation of image details compared to existing techniques.
Effective noise suppression is achieved simultaneously with detail preservation.
Successful validation on both synthetic and real-world image data.

Conclusions:

The developed super-resolution method offers significant improvements in image quality.
The combination of adaptive loss functions and advanced image priors is key to success.
This approach provides a robust solution for practical super-resolution applications.