Wavelets have been widely utilized for image analysis and image/video coding. In this paper, we describe how wavelet transform (WT) can facilitate the important computer vision technique - stereo vision - by establishing valid constraints in the WT domain and combining some psychophysical knowledge of the human visual system. Because of the displacement presented in the stereo images, continuous WT is suited for stereo image processing and analysis. Four constraints are established and called the smooth component gradient constraints (SCGC) and smooth component Laplacian constraints (SCLC), respectively. To derive them, geometric conditions and psychophysical knowledge for human binocular visual information fusion are combined. These constraints greatly improve the efficiency of stereo matching: matching accuracy and speed is greatly improved, and matching robustness in noisy environment is significantly improved too, compared with traditional algorithms without those constraints. Experiments show encouraging results. The paper discuses the possibility of establishing some further constraints in the WT domain too.
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