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Implementation of generalized cross validation based image denoising in parallel virtual machine environment

机译:并行虚拟机环境中基于通用交叉验证的图像去噪的实现

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摘要

Paper presents a wavelet based, high performance image denoising scheme implemented in a parallel virtual machine (PVM) environment. Performance of generalized cross validation (GCV) based threshold selection scheme is poorer from CPU time viewpoint when implemented sequentially. In contrast to the traditional parallel approaches, which rely on specialized parallel machines, the present work explores the potential of distributed systems for parallelism. The master/slave model is adopted for control of machines. In this paper the performance of GCV based thresholding algorithm is presented in PVM environment. The proposed algorithm outperforms Donoho and Johnstone's Universal and SureSrink thresholding scheme in most of the test cases. The present investigation validates suitability of GCV thresholding scheme from parallel implementation viewpoint. (C) 2003 Elsevier Inc. All rights reserved. [References: 16]
机译:论文提出了在并行虚拟机(PVM)环境中实现的基于小波的高性能图像去噪方案。从CPU时间的角度出发,按顺序实施时,基于通用交叉验证(GCV)的阈值选择方案的性能较差。与依赖于专用并行机的传统并行方法相比,本工作探索了分布式系统实现并行的潜力。采用主/从模型控制机器。本文介绍了基于GCV的阈值算法在PVM​​环境中的性能。在大多数测试案例中,所提出的算法均优于Donoho和Johnstone的Universal和SureSrink阈值方案。本研究从并行实现的角度验证了GCV阈值方案的适用性。 (C)2003 Elsevier Inc.保留所有权利。 [参考:16]

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