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. 2022 Jun 21:2022:8303496.
doi: 10.1155/2022/8303496. eCollection 2022.

Analysis of Chinese Painting Color Teaching Based on Intelligent Image Color Processing Technology in the Network as a Green Environment

Li Tian  1
Affiliations

Analysis of Chinese Painting Color Teaching Based on Intelligent Image Color Processing Technology in the Network as a Green Environment

Li Tian. J Environ Public Health. .

Abstract

This work was conducted to study the Chinese painting color teaching analysis of intelligent image color processing technology under the network environment. First, the paper preprocesses the obtained color mural images, realizes the automatic recognition and marking of the images with different defect degrees and color fading, and uses denoising and texture background elimination to remove unnecessary background information. Then, according to the characteristic that the repair order of boundary points in the Criminisi algorithm is determined by the size of priority weight, the data items and confidence items are added. Finally, the design uses image processing technology and the loss formula to identify the connecting edge of the color area to be taught, establish the color extraction area, calculate the bit weight of the best color, find out the color extraction position, and synthesize different colors according to the original painting color superposition method. The partial differential equation is used to set the teaching code of color teaching system to realize the teaching of Chinese painting color. The experimental results show that compared with the original teaching system, the designed color teaching system has a stronger ability to recognize the edge of Chinese painting color teaching, and the quality of Chinese painting after teaching is higher. It can be seen that the color teaching system can be applied to the color teaching of Chinese painting.

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Conflict of interest statement

The author declares that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Image color processing technology.
Figure 2
Figure 2
Relationship between computer graphics and related disciplines.
Figure 3
Figure 3
Basic model of image processing.
Figure 4
Figure 4
The mathematical model of image teaching problem.
Figure 5
Figure 5
Problems in continuous priority treatment of strong edges. The original image (a), mark the patched area (b), and the result image (c).
Figure 6
Figure 6
Normalization process of the image object.
Figure 7
Figure 7
Flowchart of color processing.
Figure 8
Figure 8
Three-dimensional analysis of traditional Chinese painting.
Figure 9
Figure 9
Color transition trajectory.
Figure 10
Figure 10
Monitoring error results.
See this image and copyright information in PMC

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