Parametric indices of fuzziness for automated image enhancement

• Published in: Fuzzy sets and systems Vol. 157; no. 8; pp. 1126 - 1138
• Main Authors: Vlachos, Ioannis K., Sergiadis, George D.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.04.2006; Elsevier
• Subjects: Applied sciences; Circuit properties; Computer science; control theory; systems; Contrast enhancement; Control theory. Systems; Digital circuits; Electric, optical and optoelectronic circuits; Electronic circuits; Electronics; Exact sciences and technology; Fuzzy complements; Image processing; Information, signal and communications theory; Mathematical methods; Measures of fuzziness; Miscellaneous; Parametric indices of fuzziness; System theory; Telecommunications and information theory; Theoretical computing; Measures of fuzziness; Fuzzy complements; Image processing; Contrast enhancement; Parametric indices of fuzziness; Fuzzy system; Optimization method; Fuzzy set; Algorithm; Engineering; Experimental result; Information processing; Optimality criterion
• ISSN: 0165-0114; 1872-6801
• DOI: 10.1016/j.fss.2005.11.016

This paper presents an automated fuzziness-driven algorithm for image enhancement. A class of parametric indices of fuzziness is introduced, which serves as the optimization criterion of the contrast-enhancement procedure. We show that the parametric class of indices constitutes a one-parameter generalization of the linear index of fuzziness of a set. The modification of the membership values of image pixels in the fuzzy plane is performed by finding the optimal S-function, which maximizes the parametric index of fuzziness (PIF). The first-order fuzzy moment of the image is used for tuning the PIF. Experimental results demonstrate the efficiency of the proposed framework in enhancing even highly low-contrasted images and also its ability to improve existing contrast-enhancing algorithms.