Disparity estimation for binocular images is an important problem for many visual tasks such as 3D environment reconstruction, digital hologram, virtual reality, robot navigation, etc. Conventional approaches are based on brightness constancy assumption to establish spatial correspondences between a pair of images. However, in the presence of large illumination variation and serious noisy contamination, conventional approaches fail to generate accurate disparity maps. To have robust disparity estimation in these situations, we first propose a model - color monogenic curvature phase to describe local features of color images by embedding the monogenic curvature signal into the quaternion representation. Then a multiscale framework to estimate disparities is proposed by coupling the advantages of the color monogenic curvature phase and mutual information. Both indoor and outdoor images with large brightness variation are used in the experiments, and the results demonstrate that our approach can achieve a good performance even in the conditions of large illumination change and serious noisy contamination.
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