In this paper, a new algorithm for accurate optical flow estimation using discrete wavelet approximation is proposed. In traditional gradient-based approaches, the computation of optical flow depends on minimizing the image and smoothness constraints. The proposed method takes advantages of the nature of wavelet theory, which can efficiently and accurately represent “things,” to model optical flow vectors and image related functions. Each flow vector and image function will be represented by linear combinations of wavelet basis functions. From such wavelet-based approximation, the leading coefficients of these basis functions carry the global information of the approximated “things.” The proposed method can successfully convert the problem of minimizing a constraint function into that of solving a linear system of a quadratic and convex function of wavelet coefficients. Once all the corresponding coefficients are decided, the flow vectors can be determined accordingly. Experiments have been conducted on both synthetic and real image sequences and the results have reflected that our approach outperformed the existing methods in terms of accuracy.