Parallel gradient domain processing of massive images
Parallel gradient domain processing of massive images
复制标题
海量图像的并行梯度域处理
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Valerio Pascucci
中科院分区：
文献类型：
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作者：
Sujin Philip;B. Summa;P. Bremer;Valerio Pascucci
即使对于相对较小的图像，梯度域处理仍然是一种计算成本特别高的技术。当图像尺寸变得巨大（千兆或万亿像素）时，这些问题变得特别麻烦，并且最好的串行技术需要花费数小时或数天的时间才能计算出解决方案。在本文中，我们为并行梯度域处理提供了一个简单的框架。具体来说，我们提供了一种并行核外方法，用于在并行 MPI 环境中无缝拼接十亿像素全景图。与现有技术不同，该框架提供了简单的实现，对所需/分配的资源保持严格控制，并且不对收敛到可接受图像的速度做出假设。此外，该方法显示了从几个到数百个内核的良好弱/强扩展，并且可以在各种硬件上运行。
Gradient domain processing remains a particularly computationally expensive technique even for relatively small images. When images become massive in size, giga or terapixel, these problems become particularly troublesome and the best serial techniques take on the order of hours or days to compute a solution. In this paper, we provide a simple framework for the parallel gradient domain processing. Specifically, we provide a parallel out-of-core method for the seamless stitching of gigapixel panoramas in a parallel MPI environment. Unlike existing techniques, the framework provides both a straightforward implementation, maintains strict control over the required/allocated resources, and makes no assumptions on the speed of convergence to an acceptable image. Furthermore, the approach shows good weak/strong scaling from several to hundreds of cores and runs on a variety of hardware.