Stereo correspondence matching: Balanced multiwavelets versus unbalanced multiwavelets

This paper investigates the efficiency of unbalanced versus balanced multiwavelets in stereo correspondence matching. A multiwavelet transform is first applied to a pair of stereo images to decorrelate them into a number of subbands. Information in the approximation subbands of an unbalanced multiwavelet carries different spectral content of the input image while the balanced multiwavelet approximation subbands produce similar spectral content of the input image. Hence, the application of the approximation subbands of the unbalanced multiwavelets in disparity map generation could produce more accurate results compared to that of balanced multiwavelets. A global error energy minimization technique is employed to generate a disparity map for each approximation subband. The information in the resulting disparity maps is then combined using a Fuzzy algorithm to generate a dense disparity map. Simulation results show that the unbalanced multiwavelets produce a smoother disparity map with less mismatch errors compared to that of balanced multiwavelets.