Research On Double Pixel Compressed Imaging System

In the last few decades, Nyquist sampling theorem guide a digital revolution, however, with the emergence of new applications, and the continuous requirements of improve the existing applications, we need to deal with larger and more high dimensional data, building equipment satisfy the need sampling rate often too expensive, even physically impossible. In recent years, there emergence a new Compressed Sensing theory caused extensive research and attention, which can use far lower sampling numbers than the Nyquist sampling theorem requires, at the same time, accurate resume the signal with the larger probability.This paper introduces the theory of Compressed Sensing, and expounds the basic process of Compressed Sensing signal processing, emphatically analyzes the three key aspects of the theory and the methods they use. And then focuses on the application of optical imaging of Compressed Sensing, and analyzes the imaging theory of single pixel camera, and points out that this system existing micromirror calibration, system noise, and completely 3-D not related projection design defect, to improve its deficiency, we presents a double pixel camera scheme. Through a series of steps to build the actual double pixel compressed imaging system, mainly including: component selection, design signal acquisition conversion module circuit, light adjust. Experiments show that the system can achieve the desired effect in imaging application.This paper also has researched on the Compressed Sensing measure matrixes and reconstruction algorithms. By analysis the structure and properties of the existing measure matrixes, find out the one suitable for compressed imaging. Based on Scrambled block Hadamard ensemble, this paper proposes a fast, highly sparse sampling operator, and the simulation experiment shows that the operator can perfect reconstruction signal meanwhile need approximate optimal sample frequency measurement.