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Illumination-Invariance Optical Flow Estimation Using Weighted Regularization Transform

Many recent variational optical flow methods are not robust for illumination variance, and they only consider local image relation in terms of illumination. In this paper, we propose a new efficient illumination-invariance total variation optical flow method called the weighted regularization transform, which uses and optimizes the Weber’s Law. Our method exploits unequal probability as the weight that has non-local information to estimate stable optical flow despite illumination changes. The proposed method uses a coarse-to-fine pyramid model to reduce the influence on the data term from illumination. Then, an energy optimization procedure is introduced to constrain the minimization of the data term with the non-local regularization. Experimentation with the proposed method has been performed on three optical flow datasets and a face liveness detection database, which have challenging illumination variations, and the results demonstrate that the proposed method is quite robust with respect to variations in illumination.