Robust principal component analysis based on orthogonal inexact Lagrange multiplier method

In order to further optimize the robust principal component analysis algorithm, a robust principal component analysis algorithm based on the orthogonal inexact Lagrange multiplier method was proposed. The collected image sequences of flat bottom hole defects were processed, and compared with the results of traditional image sequence processing algorithms including polynomial fitting, principal component analysis, independent component analysis and pulse phase method. The performance of each image sequence processing algorithm was quantitatively analyzed from evaluation indicators such as defect detection rate, peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE) and entropy. The results show that each evaluation index of the robust principal component analysis algorithm based on the orthogonal inexact Lagrange multiplier method is optimal, in which the defect detection rate, PSNR, RMSE and entropy are optimized by 9.09%, 1.14%, 11.34% and 4.60% respectively compared with the suboptimal values.