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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Parallel Branch-Cut Algorithm Based on Simulated Annealing for Large-Scale Phase Unwrapping
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Parallel Branch-Cut Algorithm Based on Simulated Annealing for Large-Scale Phase Unwrapping

机译：基于模拟退火的并行分支剪切算法在大规模相位展开中的应用

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摘要

Two-dimensional phase unwrapping is a key step in the phase extraction process, an image-processing stage that is common to many different systems. Many varied approaches have been proposed over the past several decades. However, with the growth of image scale, it poses new challenges in terms of computational and memory requirements to phase unwrapping that require a global approach to obtain good results. Owing to only a single process used in most previous algorithm implementations, it becomes more problematic to unwrapping when the required computing resources exceed the capability of one computer. Meanwhile, with the development and application of supercomputer techniques, high-performance computing is emerging as a promising platform for scientific applications. In this paper, a novel hybrid multiprocessing and multithreading algorithm is proposed in order to overcome the problem of unwrapping large data sets. In this algorithm, we improve on Goldstein's branch-cut algorithm using simulated annealing idea to further optimize the set of branch cuts in parallel. For large data sets, the tiling strategy based on the nature of parallel computing guarantees the globality of phase unwrapping and avoids large-scale errors introduced. Using real and simulated interferometric data, we demonstrate that our algorithms are highly competitive with other existing algorithms in speed and accuracy. We also demonstrate that the proposed algorithm can be efficiently parallelized and performed across nodes in a high-performance computing cluster.
机译：二维相位展开是相位提取过程中的关键步骤，这是许多不同系统共有的图像处理阶段。在过去的几十年中，已经提出了许多不同的方法。但是，随着图像比例的增长，在相位展开的计算和存储要求方面提出了新的挑战，需要全局方法来获得良好的结果。由于大多数以前的算法实现中只使用一个进程，所以当所需的计算资源超过一台计算机的能力时，展开就变得更加困难。同时，随着超级计算机技术的发展和应用，高性能计算正逐渐成为有前途的科学应用平台。本文提出了一种新颖的混合多处理和多线程算法，以解决解开大数据集的问题。在该算法中，我们使用模拟退火思想对Goldstein的分支割算法进行了改进，以进一步优化并行的分支割集。对于大数据集，基于并行计算性质的切片策略可确保相位展开的全局性，并避免引入大范围的错误。使用真实的和模拟的干涉数据，我们证明了我们的算法在速度和准确性上与其他现有算法极具竞争力。我们还证明了所提出的算法可以在高性能计算集群中的各个节点之间高效并行化和执行。

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