Research on Computed Tomography Image Reconstruction Algorithm Based on Optical Flow Equation and Target Matching

Abstract

Image is the main source of human access to information. Due to the development needs of aerospace, biomedical engineering, industrial detection, culture and art, pattern recognition and military, image processing has attracted more and more attention, and has gradually developed into a promising new discipline. For different processing purposes, digital image processing can be divided into geometric processing, arithmetic processing Image coding, image enhancement, image restoration, image reconstruction, image segmentation and image analysis. Based on the adaptive image matching and tracking algorithm, a comprehensive algorithm combining particle prediction and template image update is studied to predict the position of nonlinear and non Gaussian problems. Aiming at the problem of large amount of calculation of the algorithm, an update combination method is adopted. Computed tomography technology is widely used in medical and industrial non destructive testing. Reconstruction algorithm is the core, and incomplete angle reconstruction is a hot and difficult problem in the research field of reconstruction algorithm in practical application This paper introduces the basic theoretical conclusions and common algorithms of sparse optimization. Then, the application of sparse optimization theory in image incomplete angle reconstruction is summarized, and its main research results and the role of sparse optimization are introduced; Finally, the research of incomplete angle reconstruction based on sparse optimization is prospected.