Reegan Jebadass Johnson Jebadass

This paper proposes a new intuitionistic fuzzy-based algorithm for color image enhancement. A unique aspect of the proposed method is determining the degree of hesitation using Yager’s intuition-istic fuzzy generator technique. Here a boosting parameter α is used to stimulus the intuitionistic fuzzy image for enhancement technique. In this proposed method, first normal fuzzification transforms the given image into a fuzzy image. Then it is again transferred to an intuition-istic fuzzy image. Finally, one can get the proposed enhanced image after applying contrast limited adaptive histogram equalization. The proposed method is compared with other existing methods like brightness preserving dynamic fuzzy histogram equalization, histogram equalization , contrast limited adaptive histogram equalization, histogram specification approach, dehazing algorithm, intuitionistic fuzzy algorithm, and interval-valued intuitionistic fuzzy algorithm. We got better results through performance analyses like entropy, structural similarity index, absolute mean brightness error, and contrast improvement index. The outcomes uncovered that the proposed technique beats other existing strategies on generally speaking execution measurements and visual quality. Mathematics Subject Classification (2020): 68U10 . 94D05