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The compressive sensing (CS) technique is a novel tool used to reconstruct images using fewer samples, normally sparse in the transform domain, than those required by conventional imaging systems. However, the methods applied for signal reconstruction within the CS approach still present some problems in the implementation, mainly due to their intensive computational demand and high power consumption requirements. These drawbacks need addressing if this approach is followed in systems aimed at e.g. drone autonomous flying or other embedded applications that additionally require very short processing times. In this paper we evaluate the use of hardware based parallel processing architecture for the implementation of the Orthogonal Matching Pursuit (OMP) algorithm, one of the most efficient CS reconstruction algorithms developed so far. To improve the algorithm performance, we target different maximum allowed processing times to reach minimum image resolutions required by each system of interest using different sparse (16 and 64) amounts of single-pixel generated samples per image. We also target the final image resolution to be above 20 dB in terms of the peak signal-to-noise ratio (PSNR). To reduce the execution and processing times required to generate each image, we propose implementing parallel kernels in the hardware platform for each of the operations required by the algorithms under study. In the proposed implementation the reconstructed images are used to generate video streams that form the foundation on which decisions are to be made by the system in continuous time, whereby each single image (frame) reconstruction cannot overcome 30 ms in order to maintain the minimum amount of frames per second (fps) above 33 (minimum required for an acceptable video stream). The implementation of a variation of the OMP algorithm in a graphics processing unit (GPU) using parallel architecture approach allows obtaining processing times 4 or 5 times shorter than those obtained if central processing unit (CPU) based architecture implementation is used for the same purpose.
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