Decision tree-based contrast enhancement for various color images

Chun Ming Tsai, Zong-Mu Yeh, Yuan Fang Wang

研究成果: 雜誌貢獻文章

14 引文 斯高帕斯(Scopus)

摘要

Conventional contrast enhancement methods are application-oriented and they need transformation functions and parameters which are specified manually. Furthermore, most of them do not produce satisfactory enhancement results for certain types of color images: dark, low-contrast, bright, mostly dark, high-contrast, and mostly bright. Thus, this paper proposes a decision tree-based contrast enhancement algorithm to enhance the above described color images simultaneously. This method includes three steps: first, statistical image features are extracted from the luminance distribution. Second, a decision tree-based classification is proposed to divide the input images into dark, low-contrast, bright, mostly dark, high-contrast, and mostly bright categories. Finally, these image categories are handled by piecewise linear based enhancement method. This novel enhancement method is automatic and parameter-free. Our experiments included different color and gray images. Experimental results show that the performance of the proposed enhancement method is better than other available methods in skin detection, visual perception, and image subtraction measurements.

原文英語
頁(從 - 到)21-37
頁數17
期刊Machine Vision and Applications
22
發行號1
DOIs
出版狀態已發佈 - 2011 一月 1

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

指紋 深入研究「Decision tree-based contrast enhancement for various color images」主題。共同形成了獨特的指紋。

  • 引用此