Alphatech Inc., Burlington, MA.
IEEE Trans Image Process. 1994;3(1):41-64. doi: 10.1109/83.265979.
A new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. The solution of the new problem formulation is computed with an efficient multiscale algorithm. Experiments on several image sequences demonstrate the substantial computational savings that can be achieved due to the fact that the algorithm is noniterative and in fact has a per pixel computational complexity that is independent of image size. The new approach also has a number of other important advantages. Specifically, multiresolution flow field estimates are available, allowing great flexibility in dealing with the tradeoff between resolution and accuracy. Multiscale error covariance information is also available, which is of considerable use in assessing the accuracy of the estimates. In particular, these error statistics can be used as the basis for a rational procedure for determining the spatially-varying optimal reconstruction resolution. Furthermore, if there are compelling reasons to insist upon a standard smoothness constraint, the new algorithm provides an excellent initialization for the iterative algorithms associated with the smoothness constraint problem formulation. Finally, the usefulness of the approach should extend to a wide variety of ill-posed inverse problems in which variational techniques seeking a "smooth" solution are generally used.
引入并发展了一种新的图像处理正则化方法，该方法使用计算图像序列中密集光流场的问题作为载体。新问题公式的解是通过高效的多尺度算法计算的。在几个图像序列上的实验表明，由于该算法是非迭代的，实际上具有与图像大小无关的像素级计算复杂度，可以实现大量的计算节省。该新方法还具有许多其他重要优势。具体来说，可以获得多分辨率流场估计，从而在分辨率和准确性之间的权衡中具有很大的灵活性。还可以获得多尺度误差协方差信息，这对于评估估计的准确性非常有用。特别是，这些误差统计信息可以用作确定空间变化的最优重建分辨率的合理过程的基础。此外，如果有强烈的理由坚持标准平滑约束，那么新算法为与平滑约束问题公式相关的迭代算法提供了极好的初始化。最后，该方法的用途应该扩展到各种不适定的逆问题，其中通常使用寻求“平滑”解的变分技术。
