Decision tree-based contrast enhancement for various color images

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.