The Design of Compressed Sensing Image Reconstruction Algorithm Based on Structure Priori Model

Abstract:

Compressed sensing is referred to as the CS technology; it can realize image compression and reconstruction process in low sample rate. It has great potential to reduce the sampling rate and improve the quality of image processing. In this paper, we introduce the structure prior model into the compressed sensing and image processing, and make the image reconstruction of high dimensional optimization process simplified into a series of low dimensional optimization process, which improves the processing speed and image quality. In order to verify the effectiveness and reliability of the proposed algorithm, this paper uses combined control form of C language and MATLAB software to design the programming of structure prior model, and use the Simulink environment to debug the program. Through the calculation we get the image block and the reconstruction result. It provides the technical reference for the research on image compressed sensing technology.

• Periodical: Applied Mechanics and Materials (Volumes 602-605)
• Pages: 3311-3315
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.602-605.3311
• Online since: August 2014
• Authors: Lin Zhang, Xia Ling Zeng*
• Keywords: Compressed Sensing, CS Technology, High Dimensional Optimization, Image Reconstruction, Sampling Rate, Structure Priori

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