PVM Implementation of DWT-Based Image Denoising

Abstract

Recently users from both high-performance scientific community and general-purpose applications have shown keen interest in parallel processing due to its higher performance, lower cost, and sustained productivity [148]. To solve a computationally intensive problem efficiently on a cluster of existing computers, distributed computing involves a significantly lower cost factor [156]. Although it is difficult for a distributed computing user to achieve the computational capacity of large massively parallel processors (MPP), it is possible to solve large-size problems by combining a variety of distributed computing resources, connected by high-speed networks. This approach has advantages in terms of flexibility, scalability, and low cost. The advantage of using a cluster of workstations as the computational platform is that a cluster of a large number of workstations is easily available. A disadvantage is that there may be many users running unrelated tasks on the workstations so that the available computing resource for each task fluctuates in an unpredictable manner. Furthermore, communication between workstations is relatively slow. Although the performance of generalized cross-validation (GCV)–based threshold selection scheme is excellent, it is costly from CPU time viewpoint when implemented sequentially. In contrast to the traditional parallel approaches, which rely on specialized parallel machines, present work explores the potential of distributed systems for parallelism. The master/slave model is adopted for control of machines. This chapter is organized as follows. Section 4.1 presents background material and review of work in related area. Section 4.2 presents parallel algorithm of DWT. Basics of PVM programming has been discussed in Sect. 4.3.