This paper presents a fuzzy relaxation algorithm, which is based on the possibility and FAM instead of he probability and compatibility coefficients used in most of existing probabilistic relaxation algorithms, Because of eliminating stages for estimating of compatibility coefficients and normalization of the probability estimates, the proposed fuzzy relaxation algorithms increases the parallelism and has a simple iteration scheme. The construction of fuzzy relaxation scheme consists of the following three tasks: (1) definition of in/output linguistic variables, their term sets, and possibility. (2) Definition of FAM rule bases for relaxation using fuzzy compound relations. (3) Construction of the iteration scheme for calculating the new possibility estimate. Applications to region segmentation an ege detectiojn algorithms show that he proposed method can be used for not only reducing the image ambiguity and segmentation errors, but also enhancing the raw edge iteratively.