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 comprises an optical unit (I), an electrical unit (II) and a rear-end data pipeline processing unit (III) which are subjected to parallel pipeline processing, wherein the optical unit (I) is used for collecting an optical signal to obtain a target image signal, and segmenting and modulating the target image signal and then sending the segmented and modulated target image signal to the electrical unit (II); the electrical unit (II) is used for performing parallel complementary compressed sensing imaging, completing parallel complementary measurement and sending low-resolution image data to the rear-end data pipeline processing unit (III); and the rear-end data pipeline processing unit (III) is used for realizing high-resolution image reconstruction and real-time display by using a GPU accelerated compressed sensing high-speed reconstruction algorithm. According to the invention, a parallel pipeline scheme of the optical unit, the electrical unit and the GPU accelerated reconstruction part is used, so that compressed sensing real-time acquisition of low-resolution images and real-time reconstruction and display of high-resolution images are realized.

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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