Stereo Correspondence Matching Using Multiwavelets

this paper presents a novel multiwavelet-based stereo correspondence matching technique. A multiwavelet transform is first applied to a pair of stereo images to decorrelate the images into a number of approximation (baseband) and detail subbands. Information in the basebands is less sensitive to shift variability of the multiwavelet transform. Basebands of each input image carry different spectral content of the image. Therefore, using the basebands to generate the disparity map is likely to produce more accurate results. A global error energy minimization technique is employed to generate a disparity map for each baseband of the stereo pairs. Information in the resulting disparity maps is then combined using a Fuzzy algorithm to construct a dense disparity map. A filtering process is finally applied to smooth the disparity map and reduce its erroneous matches. Middlebury stereo test images are used to generate experimental results. Results show that the proposed technique produces smoother disparity maps with less mismatch errors compared to applying the same global error energy minimization technique to wavelet transformed image data.