Semi-Tensor Product Compressed Sensing with Its Applications: A Review

Recently, as an emerging signal processing technology, the semi-tensor product compressed sensing (STP-CS) has attracted widespread attention in the fields of image processing, communications, and bioinformatics. This paper reviews the theoretical foundations, algorithmic designs, and practical applications of STP-CS. It begins by revisiting the basic concepts of compressed sensing (CS) and the definition of the semi-tensor product (STP), followed by a detailed discussion on the theoretical model of STP-CS, optimization of the measurement matrix, and reconstruction algorithms. Furthermore, the paper explores the practical applications of STP-CS in areas such as sensor nodes, visual security, image encryption, and spectrum sensing, analyzing its performance advantages and potential challenges in these fields. A comprehensive analysis indicates that STP-CS offers significant benefits in saving storage space, reducing computational complexity, and enhancing data security, making it a promising technology in the field of signal processing.