说明：  字典学习与快速正交匹配追踪。正交匹配追求 （OMP） 在过去十年中已被证明是图像和信号处理领域的重要算法，用于估计字典学习中的稀疏表示。多年来，加快 OMP 算法的精度的努力一直通过通用 OMP （g-OMP） 和快速 OMP （f-OMP） 等变体进行。所有这些算法都递归地求解了"S"样本数中每个信号样本的OMP。该算法，快速 OMP （r-OMP），运行"N"原子的循环，同时估计所有样本，并在自N<<S以来的真实场景中，建议的方法将OMP的速度加快几个数量级。
(Orthogonal Matching Pursuit (OMP) has proven itself to be a significant algorithm in image and signal processing domain in the last decade to estimate sparse representations in dictionary learning. Over the years, efforts to speed up the OMP algorithm for the same accuracy has been through variants like generalized OMP (g-OMP) and fast OMP (f-OMP). All of these algorithms solve OMP recursively for each signal sample among 'S' number of samples. This algorithm, rapid OMP (r-OMP), runs the loop for 'N' atoms, simultaneously estimating for all samples, and, in a real scene since N<<S, the proposed approach speeds up OMP by several orders of magnitude.)