An ink-diffusion-based rendering method for Chinese ink painting

Yao Zhang; Zhenjiang Miao; Yanghua Liu; Wei Zhou

doi:10.1117/12.976037

8 June 2012 An ink-diffusion-based rendering method for Chinese ink painting

Yao Zhang, Zhenjiang Miao, Yanghua Liu, Wei Zhou

Author Affiliations +

Proceedings Volume 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012); 83344L (2012) https://doi.org/10.1117/12.976037
Event: Fourth International Conference on Digital Image Processing (ICDIP 2012), 2012, Kuala Lumpur, Malaysia

Abstract

In non-photorealistic rendering (NPR), the Chinese ink painting style rendering is a traditional NPR skill of China. In this paper, we propose a method for image-based and ink-diffusion-based Chinese ink painting NPR. Users without painting experience can also convert a normal image to a Chinese ink painting automatically. As we known, ink is an important pigment for Chinese ink painting and the various ink shade effects on the painting are produced by ink mixed with water. In addition, the ink diffusion along the boundaries is a very important aspect of Chinese ink painting. In order to realize the effects described above, we present a Chinese ink painting NPR method based on ink diffusion. We use Mean Shift based image segmentation algorithm to preprocess the input image to get regions with different tones. Then, we detect the segmentation regions' edges letting the edge points to be the start points for diffusion. We set each point an ink value which is corresponding to its gray value. At the same time, a new algorithm simulating ink diffusion is proposed to make the segmentation image look like a black-ink painting. Results in this paper demonstrate our method is promising.

Citation Download Citation

Yao Zhang, Zhenjiang Miao, Yanghua Liu, and Wei Zhou "An ink-diffusion-based rendering method for Chinese ink painting", Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83344L (8 June 2012); https://doi.org/10.1117/12.976037

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE