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

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.

I. Introduction

Computed tomography is a perspective imaging technology, which can image the internal structure of an object non-contact and non-destructive [1] CT image reconstruction is one of the key technologies of imaging system, involving physics, mathematics, computer graphics and other disciplines Image reconstruction from the collected projection data is the core theory and basic algorithm of imaging [2]. As one of the core topics in the field of computer vision, video image moving target tracking integrates advanced technologies in several fields such as image processing, artificial intelligence, pattern recognition, automatic control and computer application, and is widely used in video surveillance, military visual guidance, robot human visual navigation, safety detection, traffic flow monitoring and so on [3]. Target tracking in sequence images has always been a very active topic in the fields of computer vision, image processing and pattern recognition [4]. It has a wide range of applications, such as military target tracking, industrial product monitoring, traffic intersection monitoring and so on. Matching tracking algorithm is an image tracking algorithm with large amount of calculation and high reliability [6]. Whether high-quality reconstructed images can be obtained efficiently directly affects the further processing of imaging object image segmentation, registration and fusion, and is also closely related to the effectiveness of CT imaging system in practical application [7] Therefore, CT image reconstruction algorithm has always been a hot issue in the field of CT imaging, and the development of reconstruction algorithm will even bring fundamental changes to the whole CT imaging system [8].