A Linguistic Color Space for Image Enhancemenet

Color is an evitable feature of image and its prediction is still a critical issue in computer vision and image processing. It is necessary to ensure that the perceived color of an object remains constant under varying conditions. Novelty of this paper lies in introduction of a linguistic color space using mamdani's fuzzy inference system for better color constancy and image enhancement. We define different types of membership functions with minimum number of inference rules to map RGB components to linguistic color space. Also, psychophysically measured colors are represented in terms of linguistic variables. While evaluating the algorithm, it is clear that this algorithm rivals current state of art performance without the help of training data. In addition, this method can be used for clouddetection of aerial images, thus opening further research in aerial image processing.