Abstract
Compressed sensing theory is a new signal processing theory in recent years, which is the birth of the signal processing field. Compared to the traditional Nyquist sampling rate, with little sample data quantity, compressed sensing theory saves subsequent processing time and storage space, making it a broad application prospect in the signal processing field. This paper first discuss the three key problems of the application of the compressed perception theory: signal sparse representation, machine measurement matrix design and signal reconstruction algorithm, preliminarily study the application of the compression perception theory in image compression technology, and giving the reconstructed image under different compression rate and PSNR. Computer simulation results show the feasibility of theory.
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