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The physics-based optical flow (PBOF) model was derived from typical flow visualizations and it provided a more solid physical foundation for optical flow calculation. However, like many recent variational optical flow methods, it is not formulated to deal with the effect of illumination variation. In this paper, we propose a new efficient illumination-invariant optical flow estimation method, which improves the physics-based optical flow model. The proposed method introduces a multiplicative product of the velocity gradient and intensity from PBOF and an additive diffusion component to relax the brightness constancy assumption. The proposed method employs a linear transformation to reduce the influence of varying illumination on the data term. Then, a smoothness-sparsity regularization is used to constrain the minimization problem to have good edge-preserving for optical flow. Furthermore, the numerical implementation is given to solve the optimization problem. The accuracy of the developed method and the robustness against varying illumination are evaluated. The proposed method can provide motion estimation with acceptable accuracy for three optical flow datasets and cloud images of Jupiter's great red spot, which have challenging illumination variations. • More solid physical foundation for optical flow estimation. • A linear transformation to reduce the influence of varying illumination. • A smoothness-sparsity regularization to achieve good edge-preserving. • Flow patterns extracted from Jupiter's great red spot. [ABSTRACT FROM AUTHOR]

A variational solution of the physics-based optical flow equation is studied for extraction of high-resolution velocity fields from flow visualization images. The solution to preserve discontinuities in a fluid velocity field is mathematically justified. The uniqueness of the solution and the convergence of a successive approximation sequence are proven. A numerical algorithm is developed and examined through numerical experiments. [ABSTRACT FROM AUTHOR]