目的 对不同艺术风格的模拟和绘制是非真实感绘制技术的主要任务之一，目前非真实感绘制技术已对油画、水彩画、中国书法等国内外艺术风格进行了模拟，然而对粉笔化艺术风格的模拟方法并不多见。本文提出了一种基于滤波扩散和线积分卷积（LIC）的粉笔画艺术风格绘制技术。方法 首先输入2维目标图像，通过对目标图像二值化处理、边缘提取操作，获得连续、光滑的边缘信息，并采用滤波扩散技术对边缘图像进行扩散处理，模拟粉笔画中笔划的毛糙效果，同时通过采用图像增强方法增强了笔划的细节信息；其次，由于真实粉笔画在创作时，粉笔颜料黏附在图像局部区域，形成具有方向的笔刷纹理效果，算法通过在目标图像中添加白噪声，基于线积分卷积LIC产生具有方向的粉笔画笔刷纹理，并通过形态学膨胀处理获得粉笔画的笔划纹理，模拟出粉笔画中笔划的笔触特征。再次，真实的粉笔画艺术效果往往在黑板、木材等材质中创作，算法将产生的笔刷纹理图像、色彩信息以及边缘图像通过图层映射方法，映射到黑板材质等输入背景图像中，产生最终的粉笔画艺术效果图像。结果 通过对输入2维图像进行实验，模拟出具有粉笔画艺术效果的结果图像，突出了粉笔画的线条细节信息和笔划艺术特征。结论 提出了一种粉笔画艺术效果模拟算法，非真实感绘制领域的有效补充，算法简单有效，能模拟出真实的粉笔画艺术效果，增强了艺术表现力。

Objective Non-photorealistic rendering (NPR) technique combines artistic painting rule and scientific methods. This technique can express and transfer information besides photorealistic computer graphics. The NPR also serves an increasingly important role in medical science, architectonic, and education. Many artworks, such as oil, watercolor, and Chinese calligraphy, have recently been simulated. Although numerous NPR methods have been proposed to digitize and simulate artistic works, the exploration or rendering of different artistic works remains extremely challenging and an open question. A few NPR methods can be adopted to render chalk art style. This study proposes an NPR technique that generates a chalk art style from a 2D photograph on the basis of diffusion and line integral convolution (LIC). Method Similar to existing exemplar-based methods, an input natural image is regarded as a foreground image, and an input material image is taken as a background image. We obtain the final chalk art painting through the foreground image mapping to the background image. First, continuous and smooth edge information is obtained by threshold processing and edge extraction from the target image. The edge detection method is based on the difference of the Gaussian filter. Considering that the real chalk art painting has coarse lines, we adopt the diffusion technique to simulate this characteristic. Image enhancement is also used to enhance the details of the edge information. Second, when people create a chalk painting, they usually use chalk to draw on the blackboard or other materials; the chalk pigment will then be absorbed on these materials to show some artistic illustration. Therefore, this textural characteristic of the chalk stroke should be simulated in the system. Our algorithm adds some white noise to the target image, and the chalk brush texture is simulated based on the LIC. The morphological dilation operation is used to generate the final chalk stroke texture. The real artistic effect of the chalk painting is often created in blackboard, wood, and other materials. Thus, the blackboard image is input as the background image. Based on the layer-mapping technique, the algorithm will merge the brush texture image, color image, and edge image to the blackboard image. Subsequently, the final chalk painting art style will be simulated. Result We can obtain chalk art illustrations by inputting different source images. The line details and stroke texture of the chalk characteristic can also be displayed. When the different foreground images and background images are input, different chalk illustrations can be obtained. Conclusion This study proposes an NPR method to generate chalk art style. Experimental results demonstrate the effectiveness of our method in producing chalk line stylistic illustrations. This chalk painting art style is a useful supplement to the NPR, and the rendering results advance the NPR field. People without painting experience can create chalk art painting through our simple system. The proposed method is simple, fast, and easy to implement.