28 April 2018 Comparison of multihardware parallel implementations for a phase unwrapping algorithm
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Abstract
Phase unwrapping is an important problem in the areas of optical metrology, synthetic aperture radar (SAR) image analysis, and magnetic resonance imaging (MRI) analysis. These images are becoming larger in size and, particularly, the availability and need for processing of SAR and MRI data have increased significantly with the acquisition of remote sensing data and the popularization of magnetic resonators in clinical diagnosis. Therefore, it is important to develop faster and accurate phase unwrapping algorithms. We propose a parallel multigrid algorithm of a phase unwrapping method named accumulation of residual maps, which builds on a serial algorithm that consists of the minimization of a cost function; minimization achieved by means of a serial Gauss–Seidel kind algorithm. Our algorithm also optimizes the original cost function, but unlike the original work, our algorithm is a parallel Jacobi class with alternated minimizations. This strategy is known as the chessboard type, where red pixels can be updated in parallel at same iteration since they are independent. Similarly, black pixels can be updated in parallel in an alternating iteration. We present parallel implementations of our algorithm for different parallel multicore architecture such as CPU-multicore, Xeon Phi coprocessor, and Nvidia graphics processing unit. In all the cases, we obtain a superior performance of our parallel algorithm when compared with the original serial version. In addition, we present a detailed comparative performance of the developed parallel versions.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Francisco Javier Hernandez-Lopez, Mariano Rivera, Adan Salazar-Garibay, Ricardo Legarda-Sáenz, "Comparison of multihardware parallel implementations for a phase unwrapping algorithm," Optical Engineering 57(4), 043113 (28 April 2018). https://doi.org/10.1117/1.OE.57.4.043113 Submission: Received 1 November 2017; Accepted 20 March 2018
Submission: Received 1 November 2017; Accepted 20 March 2018
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