HS(Horn&Schunck)方法是光流计算中的经典方法之一。在经典HS方法中,图像中两点间的灰度变化被假定为线性的,而实际上灰度变化是非线性的。因此,在HS算法中最小均方差迭代的最终收敛点会产生偏移,从而导致光流计算结果的不准确。为此,详细分析了灰度估计不准确造成的偏差,提出了一种改进HS算法。实验部分给出了改进算法和其他经典光流计算方法的计算结果比较。实验结果表明,改进HS算法可以得到较好的计算结果,并能明显减少光流计算的迭代次数。

HS method is one of the most classical methods for optical flow computation.In practice the variation of gray level between two points is nonlinear,while in the original HS method,it is assumed to be linear,which leads to the inaccuracy of the convergent point of LSM iterations.In the 2nd section,we discuss the reason why the classic method isn't accurate enough in detail.And in the 3rd section we present a new algorithm to improve accuracy of gray gradient by using the offset,which is generated in the previous cycle,to produce the new gradient.The following experiments,including synthesized image sequences and images sequences extracted from real video,compare the original algorithms with the improved one.The objects in the synthesized sequences involve some stereotype movements such as translation,rotation,and zooming.The results show that the veracity of optical flow field has been enhanced,and the convergence speed is accelerated to a great extend.