Zhengzhou Normal University, Zhengzhou, Henan 450000, China.
J Environ Public Health. 2022 Jun 21;2022:8303496. doi: 10.1155/2022/8303496. eCollection 2022.
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.
本工作旨在研究网络环境下智能图像色彩处理技术的中国画色彩教学分析。首先，对获取的彩色壁画图像进行预处理，实现对不同缺陷程度和颜色褪色的图像进行自动识别和标记，并采用去噪和纹理背景消除去除不必要的背景信息。然后，根据 Criminisi 算法中边界点修复顺序由优先级权重大小决定的特点，添加数据项和置信项。最后，设计使用图像处理技术和损失公式识别待教色彩区域的连接边缘，建立色彩提取区域，计算最佳色彩的位权重，找出色彩提取位置，并根据原始绘画色彩叠加方法合成不同色彩。利用偏微分方程设置色彩教学系统的教学码，实现中国画色彩教学。实验结果表明，与原教学系统相比，设计的色彩教学系统对中国画色彩教学边缘的识别能力更强，教学后的中国画质量更高。可见，色彩教学系统可应用于中国画的色彩教学。
