基于ARM NEON扩展的形态滤波快速实现
Fast Implementation of Morphological Filtering Using ARM NEON Extension
摘要：
在本文中，我们考虑加速潜力的形态学图像滤波在ARM处理器。形态学运算在图像分析和识别中有着广泛的应用，在某些情况下，形态学运算的加速可以显著减少识别的总执行时间。更具体地说，我们提出了使用ARM SIMD扩展NEON的腐蚀和膨胀的快速实现。利用矩形结构元素的这些操作是可分离的。它们是利用可分离性的优点作为连续的水平和垂直通道来实现的。每一遍都使用van Herk/Gil-Werman算法实现大窗口和低常数线性复杂度算法实现小窗口。最后的实现改进了SIMD和使用这些方法的组合。我们还考虑了使用ARM NEON实现8x8和16 x16矩阵的快速转置，以获得形态学运算的额外计算增益。实验结果表明，与无SIMD的van Herk/Gil-Werman算法相比，该算法在腐蚀和膨胀的最终实现效率提高了3倍;与无SIMD的转置算法相比，该算法在8x8矩阵转置时的加速比提高了5.7倍;
英文摘要：
In this paper we consider speedup potential of morphological image filtering on ARM processors. Morphological operations are widely used in image analysis and recognition and their speedup in some cases can significantly reduce overall execution time of recognition. More specifically, we propose fast implementation of erosion and dilation using ARM SIMD extension NEON. These operations with the rectangular structuring element are separable. They were implemented using the advantages of separability as sequential horizontal and vertical passes. Each pass was implemented using van Herk/Gil-Werman algorithm for large windows and low-constant linear complexity algorithm for small windows. Final implementation was improved with SIMD and used a combination of these methods. We also considered fast transpose implementation of 8x8 and 16x16 matrices using ARM NEON to get additional computational gain for morphological operations. Experiments showed 3 times efficiency increase for final implementation of erosion and dilation compared to van Herk/Gil-Werman algorithm without SIMD, 5.7 times speedup for 8x8 matrix transpose and 12 times speedup for 16x16 matrix transpose compared to transpose without SIMD.
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