<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, continuous wavelet transform and discrete wavelet transform are used to detect transient anomalies entrained in normal information and to demonstrate their components. Multi-scale analysis of wavelet transform, Haar wavelet basis and multi-scale edge detection algorithms are utilized to determine the modal extreme points and identify the edge points for faster and more accurate extraction of edge features of the image. In order to further validate the applicability and feasibility of wavelet transform for printing images and to determine the quality inspection criteria based on ink penetration depth and image phase anisotropy, MATLAB software is utilized to perform simulation tests. The results show that the wavelet transform can remove the noise generated by uneven illumination and printing background during the printing process and can detect the edges of the printing image with an error accuracy of ±0.063mm and meet the error correction accuracy of &lt;0.4mm as required by the printing standard. The experiments verify the feasibility of the wavelet transform, which can characterize the depth of penetration of the printing ink and the image anisotropy and provides a theoretical basis for improving the quality of printing. The experiment confirms that wavelet transform can be used to measure printing ink penetration depth and image anisotropy, giving a theoretical basis for improving printing quality.</p>
</abstract>

In this paper, continuous wavelet transform and discrete wavelet transform are used to detect transient anomalies entrained in normal information and to demonstrate their components. Multi-scale analysis of wavelet transform, Haar wavelet basis and multi-scale edge detection algorithms are utilized to determine the modal extreme points and identify the edge points for faster and more accurate extraction of edge features of the image. In order to further validate the applicability and feasibility of wavelet transform for printing images and to determine the quality inspection criteria based on ink penetration depth and image phase anisotropy, MATLAB software is utilized to perform simulation tests. The results show that the wavelet transform can remove the noise generated by uneven illumination and printing background during the printing process and can detect the edges of the printing image with an error accuracy of ±0.063mm and meet the error correction accuracy of <0.4mm as required by the printing standard. The experiments verify the feasibility of the wavelet transform, which can characterize the depth of penetration of the printing ink and the image anisotropy and provides a theoretical basis for improving the quality of printing. The experiment confirms that wavelet transform can be used to measure printing ink penetration depth and image anisotropy, giving a theoretical basis for improving printing quality.

Wavelet transform-based characterization of printing ink penetration depth and image phase dissimilarity

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, continuous wavelet transform and discrete wavelet transform are used to detect transient anomalies entrained in normal information and to...

Wavelet transform-based characterization of printing ink penetration depth and image phase dissimilarity

Online veröffentlicht: 09. Dez. 2023

Seitenbereich: -

Eingereicht: 14. Jan. 2023

Akzeptiert: 19. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01416

Schlüsselwörter
Wavelet transform, Haar wavelet basis, Edge detection, Ink penetration, Image anisotropy

© 2023 Haiyan Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Wavelet transform-based characterization of printing ink penetration depth and image phase dissimilarity

Online veröffentlicht: 09. Dez. 2023

Seitenbereich: -

Eingereicht: 14. Jan. 2023

Akzeptiert: 19. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01416

SchlüsselwörterWavelet transform, Haar wavelet basis, Edge detection, Ink penetration, Image anisotropy

© 2023 Haiyan Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Wavelet transform, Haar wavelet basis, Edge detection, Ink penetration, Image anisotropy