Contrast enhancement is an important issue in image processing, pattern recognition, and computer vision. There are two major kinds of techniques for contrast enhancement: indirect methods and direct methods. The indirect approach is to modify the histogram by changing the original gray levels to new values; it is not efficient and effective because it only stretches the global distribution of the intensities. The basic idea of the direct method is to define a measurement of the contrast and use it to enhance the contrast. Fuzzy logic has been widely applied to image processing since fuzzy set theory can handle the uncertainly, vagueness, and imprecision inherent in the images well. The proposed approach employs the fuzzy entropy principle and fuzzy set theory to automatically determine fuzzy membership functions and uses fuzzy homogeneity to define the contrast and enhance the image. We have conducted experiments on a variety of images, and the results have proved that the proposed method has better performance than the existing approaches.