We apply an AM-FM surface albedo model to analyze the projection of surface patterns viewed through a binocular camera system. This is used to support the use of modulation-based stereo matching where local image phase is used to compute stereo disparities. The local image phase is an advantageous feature for image matching, since the problem of computing disparities reduces to identifying local phase shifts between the stereoscopic image data. Local phase shifts, however, are problematic at high frequencies due to phase wrapping when disparities exceed +/-pi. We meld powerful multichannel Gabor image demodulation techniques for multiscale (coarse-to-fine) computation of local image phase with a disparity channel model for depth computation. The resulting framework unifies phase-based matching approaches with AM-FM surface/image models. We demonstrate the concepts in a stereo algorithm that generates a dense, accurate disparity map without the problems associated with phase wrapping.