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A Markov Model-Based Fusion Algorithm for Distorted Electronic Technology Archives.

¹School of Science Henan University of Technology, Zhenzhou 450000, China.

Computational Intelligence and Neuroscience

|May 2, 2022

Summary

This study introduces a novel fusion algorithm for restoring distorted electronic archives using Markov models. The enhanced nonlocal mean filtering method improves image restoration quality and classification accuracy for generated data.

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Area of Science:

Digital Archiving
Image Processing
Machine Learning

Background:

Distorted electronic archives pose challenges for data retrieval and analysis.
Traditional restoration methods struggle with complex degradation patterns.

Purpose of the Study:

To develop and evaluate an advanced algorithm for restoring distorted electronic technology archives.
To improve the quality and utility of restored text-based images and generated data.

Main Methods:

Utilizing Markov models and a fusion algorithm for restoration.
Applying image gradient parametrization and nonlocal mean filtering with improved weight function calculation.
Employing a pyramidal hierarchical network structure for internal mixed-mode learning of SAR images.
Validating online algorithm feasibility for parameter estimation using hidden Markov models.

Main Results:

The proposed algorithm demonstrates superior performance in restoring text-based images, ensuring structural similarity.
Enhanced image generation improves similarity between real and generated images.
Classification accuracy is improved using data expanded by the generation method.
Numerical experiments confirm the feasibility and accuracy of the online parameter estimation algorithm.

Conclusions:

The developed fusion algorithm effectively restores distorted electronic archives, particularly text-based images.
The method enhances the quality of generated images and improves classification tasks.
The online algorithm offers a viable solution for parameter estimation in hidden Markov models.