Edge computing SoC implementation of compressive sensing algorithm for single-pixel cameras

Garcia, A. Manjarres; Quero, C. Osorio; Rangel-Magdaleno, José; Martínez-Carranza, José; Romero, Daniel Durini

doi:10.1109/cce53527.2021.9633023

Cited by 3 publications

References 15 publications

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Real-time single-pixel imaging using a system on a chip field-programmable gate array

Hoshi

Shimobaba

Kakue

et al. 2022

Sci Rep

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Unlike conventional imaging, the single-pixel imaging technique uses a single-element detector, which enables high sensitivity, broad wavelength, and noise robustness imaging. However, it has several challenges, particularly requiring extensive computations for image reconstruction with high image quality. Therefore, high-performance computers are required for real-time reconstruction with higher image quality. In this study, we developed a compact dedicated computer for single-pixel imaging using a system on a chip field-programmable gate array (FPGA), which enables real-time reconstruction at 40 frames per second with an image size of 128 × 128 pixels. An FPGA circuit was implemented with the proposed reconstruction algorithm to obtain higher image quality by introducing encoding mask pattern optimization. The dedicated computer can accelerate the reconstruction 10 times faster than a recent CPU. Because it is very compact compared with typical computers, it can expand the application of single-pixel imaging to the Internet of Things and outdoor applications.

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Real-time single-pixel imaging using a system on a chip field-programmable gate array

Hoshi

Shimobaba

Kakue

et al. 2022

Sci Rep

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Accelerating the Fast Hadamard Single-Pixel Imaging (FHSI) on GPUs: Techniques and Optimizations

Quero,

Durini,

Rangel-Magdaleno

et al. 2024

Preprint

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The recent advancements in edge computing power are primarily attributable to technological innovations enabling accelerators with extensive hardware parallelism. One practical application is in computer imaging (CI), where GPU acceleration is pivotal, especially in reconstructing 2D images through techniques like Single-Pixel Imaging (SPI). In SPI, compressive sensing (CS) algorithms, deep learning, and Fourier transformation are essential for 2D image reconstruction. These algorithms derive substantial performance enhancements through parallelism, thereby reducing processing times. These techniques fully utilize the potential of the GPU by implementing several strategies. These include optimizing memory accessed, expanding loops for efficiency, designing effective computational kernels to reduce the number of operations, using asynchronous operations for better performance, and increasing the number of actively running threads and warps. In lab scenarios, integrating embedded GPUs becomes essential for algorithmic optimization on SoC-GPUs. This study focuses on quickly improving the Fast Hadamard Single-Pixel Imaging (FHSI) for 2D image reconstruction on Nvidia's Xavier platform. By implementing various parallel computing techniques in PyCUDA, we managed to speed up the process by approximately 10 times, significantly reducing processing times to nearly real-time levels.

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基于PnP-ADMM SOC平台的单像素高分辨成像系统

Li Miao,

Zhang Lingqiang,

Wang Xi

et al. 2024

光学学报

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Edge computing SoC implementation of compressive sensing algorithm for single-pixel cameras

Cited by 3 publications

References 15 publications

Real-time single-pixel imaging using a system on a chip field-programmable gate array

Real-time single-pixel imaging using a system on a chip field-programmable gate array

Accelerating the Fast Hadamard Single-Pixel Imaging (FHSI) on GPUs: Techniques and Optimizations

基于PnP-ADMM SOC平台的单像素高分辨成像系统

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