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An Efficient Restoration Method for the Faint Dot Matrix Images

by Ao Zhu, Peng Cao
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 45
Year of Publication: 2024
Authors: Ao Zhu, Peng Cao
10.5120/ijca2024924084

Ao Zhu, Peng Cao . An Efficient Restoration Method for the Faint Dot Matrix Images. International Journal of Computer Applications. 186, 45 ( Oct 2024), 1-10. DOI=10.5120/ijca2024924084

@article{ 10.5120/ijca2024924084,
author = { Ao Zhu, Peng Cao },
title = { An Efficient Restoration Method for the Faint Dot Matrix Images },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number45/an-efficient-restoration-method-for-the-faint-dot-matrix-images/ },
doi = { 10.5120/ijca2024924084 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-26T00:55:48+05:30
%A Ao Zhu
%A Peng Cao
%T An Efficient Restoration Method for the Faint Dot Matrix Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 45
%P 1-10
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study introduces an innovative image processing approach that integrates U-Net with Generative Adversarial Networks (GANs) for the efficient restoration and decoding of printed faint dot matrix images, aiming to enhance the application of anti-counterfeiting technologies. By merging the encoding and decoding capabilities of U-Net with the adversarial generation mechanisms of GANs, this method accurately extracts faint anti-counterfeiting features within complex noisy and blurring backgrounds, significantly improving image clarity and readability. Central to this approach are the incorporation of a Gradient-Sensitive Activation (GSA) function and a roughening term in the loss function, which are specifically optimized for high-gradient areas and detail capture. Moreover, the system dynamically adjusts network weights based on decoding rate feedback to optimize the image restoration process. Experimental results demonstrate that this method has image clarity, readability, and decoding accuracy for the faint dot matrix images. This technology has broad application prospects in industries with high security demands, such as e-commerce and product packaging.

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Index Terms

Computer Science
Information Sciences
Image Processing
Deep Learning

Keywords

GANs; U-Net; Anti-counterfeiting Technology; Gradient-sensitive Activation (GSA); Dot Matrix Image; High-gradient Image Enhancement; Security Features in Printing

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