An image fusion procedure based on fuzzy set theory that can be used to classify different image components and also to preserve and even emphasize the internal contrast is presented. Membership functions are utilized to quantitatively define the relationships between different image classes, as well as the systematic and stochastic measurement errors, in terms of pixel values. For each modality, a possibility measure is applied to determine the degree to which each pixel belongs to various image classes. These possibility measures are sent to an image fusion center, where the image components are classified and their internal contrast restored and augmented. The methodology is practically applicable even in severely noisy environments. Results generated by the proposed method illustrate its capabilities in classifying and preserving internal image details.