Mitigating the Performance and Quality of Parallelized Compressive Sensing Reconstruction Using Image Stitching

Orthogonal Matching Pursuit is an iterative greedy algorithm used to find a sparse approximation for high-dimensional signals. The algorithm is most popularly used in Compressive Sensing, which allows for the reconstruction of sparse signals at rates lower than the Shannon-Nyquist frequency, which has traditionally been used in a number of applications such as MRI and computer vision and is increasingly finding its way into Big Data and data center analytics. OMP traditionally suffers from being computationally intensive and time-consuming, this is particularly a problem in the area of Big Data where the demand for computational resources continues to grow. In this paper, the data-level parallelization of OMP through blocking is examined. Traditionally blocking has been used to ac- celerate the performance of OMP reconstruction for big data image analytics. However, as we show in this work, blocking, particularly in the form of vectorizing, introduces significant error in terms of PSNR and SSIM index in the reconstruction quality. In response, we deploy the concept of stitching to recover the lost accuracy. We further examine the influence of the level of blocking and amount of stitching (overlap between each block) with regard to recon- struction time and reconstructed image quality. While stitching boosts up the image reconstruction accuracy significantly, the ob- ject detection count results show anywhere from 11.84% to 140.54% improvement, depending on the cases being compared, it introduces significant overhead with regard to reconstruction time. To address the overhead, we deploy hardware accelerated base solutions. Given the emergence of hardware accelerators in data centers and for big data analytics in form of FPGAs, our solution effectively utilizes this resource to enhance the performance overhead of stitching by 25%. We show the minimum block size required for an FPGA speed-up.