A parallel compressed sensing gpu-accelerated real-time imaging system and method

Abstract

The invention discloses a parallel compressed sensing GPU accelerated real-time imaging system and method. The system includes an optical unit (I), an electrical unit (II) and a back-end data pipeline processing unit (III) for parallel pipeline processing; wherein, The optical unit (I) is used to collect optical signals to obtain target image signals, which are segmented and modulated and then sent to the electrical unit (II); the electrical unit (II) is used to perform parallel complementary compressed sensing imaging, Complete the parallel complementary measurement, and send the low-resolution image data to the back-end data pipeline processing unit (III); the back-end data pipeline processing unit (III) is used to realize high-resolution image reconstruction by using GPU-accelerated compressed sensing high-speed reconstruction algorithm with real-time display. The invention adopts the parallel assembly line scheme of the optical unit, the electrical unit and the GPU accelerated reconstruction component to realize the real-time collection of low-resolution images, real-time reconstruction and display of high-resolution images by compressed sensing.

Description

technical field [0001] The invention relates to the field of imaging technology, in particular to a parallel compressed sensing GPU-accelerated real-time imaging system and method, which is different from the imaging method of the traditional direct measurement computational imaging system. Background technique [0002] The imaging detection of optical signals is an important means for human beings to perceive the surrounding environment and understand the world. It is no exaggeration to say that without the birth and development of imaging technology, there would be no modern photoelectric detection technology. The era of digitalization and the intelligent Internet of Things (AIoT) drives higher and higher requirements for the time and space resolution of detectors, the scale of detection data has increased sharply, and the performance of imaging methods and photoelectric detection technologies has been continuously improved and improved in all aspects. It is also experienc...

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Problems solved by technology

[0004] However, with the further improvement of the detection resolution requirements, the optical acquisition front-end is also limited by the modulation frequency of the existing spatial optical modulation front-end. At the same time, the compressed sensing imaging model based on the compressed sensing imaging theory also faces the difficulty of constructing the measurement matrix in large-scale scenarios and the The complexity of the reconstruction algorithm is rapidly increasing, and the traditional serial single-pixel camera architecture cannot meet the demand, and the introduction of new performance-enhancing strategies is urgently needed

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