A Rule-Based Fuzzy Inference System for Adaptive Image Contrast Enhancement
Adaptive contrast enhancement (ACE) is a popular method for image contrast enhancement. In this method, enhancement is achieved by adding an amplified version of the high-frequency content of the image to its low-frequency content. The rationale behind that is supported by the fact that the human visual system is sensitive to discontinuities in images, which represent the high-frequency content of the image. Thus, emphasizing this content is expected to improve the perceived contrast. In this paper, a fuzzy ACE (FACE)-based enhancement method, FACE, is proposed. In this method, the contrast gain values are computed using a fuzzy inference system (FIS) whose parameters are entirely derived from the image local statistics. To the best of our knowledge, the computation of the ACE gain values using a FIS has never been addressed before. Experimental results have proved the capability of FACE in enhancing the image contrast with less noise amplification and overenhancement artifacts.
fuzzy inference contrast enhancement fuzzy clustering fuzzy logic