Abstract :
A Gaussian membership function is proposed to fuzzify the image information in spatial domain. We introduce a global contrast intensification operator (GINT), which contains three parameters, viz., intensification parameter t, fuzzifier fh, and the crossover point muc, for enhancement of color images. We define fuzzy contrast-based quality factor Qf and entropy-based quality factor Qe and the corresponding visual factors for the desired appearance of images. By minimizing the fuzzy entropy of the image information with respect to these quality factors, the parameters t, fh, and muc are calculated globally. By using the proposed technique, a visible improvement in the image quality is observed for under exposed images, as the entropy of the output image is decreased. The terminating criterion is decided by both the visual and quality factors. For over exposed and under plus over exposed images, the proposed fuzzification function needs to be modified by taking maximum intensity as the fourth parameter. The type of the images is indicated by the visual factor which is less than 1 for under exposed images and more than 1 for over exposed images
Keywords :
Gaussian processes; entropy; fuzzy systems; image colour analysis; image enhancement; image resolution; Gaussian membership function; color image enhancement; crossover point; entropy-based quality factor; fuzzification function; fuzzy contrast-based quality factor; fuzzy entropy; global contrast intensification operator; image information; image quality; intensification parameter; optimal fuzzy system; over-exposed images; under-exposed images; Adaptive equalizers; Color; Entropy; Fuzzy systems; Gray-scale; Histograms; Humans; Image enhancement; Image quality; Q factor; Enhancement; entropy; fuzzifier; fuzzy contrast; image quality; intensification operator; quality factor and visual factor;
