We describe an invertible non-linear image representation that is well-matched to the statistical properties of photographic images, as well as the perceptual sensitivity of the human visual system. Images are first decomposed using a multi-scale oriented linear transformation. In this domain, we develop a Markov random field model based on the dependencies within local clusters of transform coefficients, such that division of each coefficient by a particular linear combination of the amplitudes of others in the cluster reduces these dependencies. We show that the resulting divisive transformation is invertible under fairly loose conditions. Finally, we probe the statistical and perceptual advantages of this image representation, examining robustness to added noise, perceptual masking of distortions, and artifact-free automatic contrast enhancement.