Accelerating Multi-scale Image Fusion Algorithms Using CUDA

Seung-Hun Yoo, Jin-Hyung Park, Chang-Sung Jeong
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引用次数: 14

Abstract

Recently, fusion speed has emerged as an important factor in the image fusion and a substantial amount of memory and computing power are required for a high-speed fusion. This paper shows approaches to accelerate multi-scale image fusion speed on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). The GPU has evolved into a very powerful and flexible streaming processor, which provides a good computational power and memory bandwidth. We implement the multi-scale image fusion algorithms using CUDA software platform of the latest version of GPU and theirs fusion speeds are compared and evaluated with implementation in Core2 Quad processor with 2.4GHz. The GPU version achieved a speedup of 6x-8x over the CPU version.
使用CUDA加速多尺度图像融合算法
近年来,融合速度已成为图像融合的一个重要因素,高速融合需要大量的内存和计算能力。本文介绍了利用CUDA(计算统一设备架构)在GPU(图形处理单元)上加快多尺度图像融合速度的方法。GPU已经发展成为一个非常强大和灵活的流处理器,它提供了良好的计算能力和内存带宽。我们利用最新版本GPU的CUDA软件平台实现了多尺度图像融合算法,并与在2.4GHz Core2 Quad处理器上实现的融合速度进行了比较和评估。GPU版本的速度比CPU版本提高了6 -8倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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