gpu加速Nick局部图像阈值算法

2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2015-12-14 DOI:10.1109/ICPADS.2015.78

M. Najafi, Anirudh Murali, D. Lilja, J. Sartori

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引用次数: 4

摘要

二值化在文档图像处理中起着重要的作用，特别是在退化的文档图像中。在所有的局部自适应图像阈值分割算法中，Nick方法对退化的文档图像表现出了优异的二值化性能。然而，局部图像阈值分割算法，包括Nick方法，是计算密集型的，需要大量的时间来处理输入图像。在本文中，我们提出了三种CUDA GPU并行实现尼克局部图像阈值算法，以更快地对大图像进行二值化。我们的实验结果表明，与优化后的顺序实现相比，Nick方法的gpu加速实现在GeForce GTX 480上可以实现高达150倍的性能加速。

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GPU-Accelerated Nick Local Image Thresholding Algorithm

Binarization plays an important role in document image processing, particularly in degraded document images. Among all local adaptive image thresholding algorithms, the Nick method has shown excellent binarization performance for degraded document images. However, local image thresholding algorithms, including the Nick method, are computationally intensive, requiring significant time to process input images. In this paper, we propose three CUDA GPU parallel implementations of the Nick local image thresholding algorithm for faster binarization of large images. Our experimental results show that the GPU-accelerated implementations of the Nick method can achieve up to 150x performance speedup on a GeForce GTX 480 compared to its optimized sequential implementation.

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2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS)

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