Rapid Restoration of Medical Images Relying on Bayesian Personalized Sorting Algorithm

Abstract

Aiming at the real-time problem of medical image depth information restoration, which leads to the incomplete image data information collected in the process of medical image data acquisition, this study proposes a fast medical image restoration method based on the Bayesian personalized sorting algorithm (BPSA), which is used to segment the low- and high-frequency sub-band images in the initial image, The optimal low-frequency sub-band coefficient is solved by combining the nonnegative matrix decomposition method, and the high-frequency direction sub-band coefficient is solved according to the local contrast of the image and the energy in some regions, to obtain the characteristics of the medical image. The medical image information can be quickly restored by triangulation, which solves the disadvantages of too many image feature points and long operation time in the traditional image restoration method. Finally, the experimental research shows that the medical image fast restoration method proposed in this study has a better effect on texture detail restoration, which can effectively reduce the time of medical image depth information restoration and can also effectively improve the accuracy, which shows the effectiveness and practicability of the algorithm.