Image Processing Algorithm Based on Fast Sparse Low Rank and Robust PCA

 The time complexity of the algorithm is the only evaluation index in the case that the visual display effect is not very different in the actual sparse low rank processing image process. we find that the combination of fast alternating minimization (FAST PCP) and robust principal component analysis (RPCA) is a relatively fast and efficient way to use the highly efficient sparse low-rank image of the CPU, and the speed of operation is also the fastest when the computer configuration cannot be guaranteed. In this paper, the Steffensen iterative method is used to improve the FAST PCP, and the result is a more common version of FAST PCP and RPCA are better.