Evaluation of OpenCL native math functions for image processing algorithms

Damir Demirovic, Amira Serifovic-Trbalic, P. Cattin
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引用次数: 1

Abstract

Image enhancement plays an important role in different research fields such as medical image analysis. Since the same computations are usually performed on many image elements, those computations can be easily parallelized. Modern Graphics Processing Units (GPUs) are capable for doing many tasks in parallel. However, improving running times on GPUs usually leads to a loss of floating point precision. In this paper we evaluate the impact of GPU hardware implemented native functions on three GPUs, and one Central Processing Unit (CPU). As an example, the bilateral filter with built-in and native math functions was implemented and used for smoothing noisy brain Magnetic Resonance Images (MRI). For all experiments widely used error metrics were calculated. Experiments shows that native versions improve running times significantly (up to 155 times). As expected precision is lower for the measures which include a lot additions without normalization.
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评估OpenCL原生数学函数的图像处理算法
图像增强在医学图像分析等不同的研究领域发挥着重要的作用。由于通常在许多图像元素上执行相同的计算,因此这些计算可以很容易地并行化。现代图形处理单元(gpu)能够并行执行许多任务。然而,改进gpu上的运行时间通常会导致浮点精度的损失。在本文中,我们评估了GPU硬件在三个GPU和一个中央处理器(CPU)上实现本机功能的影响。作为一个例子,实现了内置和本地数学函数的双边滤波器,并将其用于平滑有噪声的脑磁共振图像(MRI)。对所有实验都计算了广泛使用的误差度量。实验表明,本机版本显著改善了运行时间(最多可提高155倍)。正如预期的那样,对于包含大量未归一化添加的度量,精度较低。
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