Performance Evaluation of Recursive Mean Filter Using Scilab, MATLAB, and MPI (Message Passing Interface)

Abstract

As a popular linear filter, the mean filter is widely used in different applications as a basic tool for image enhancement. Its main purpose is to reduce the noise in an image and thus to prepare the picture for other image-processing operations depending on the current task. In the last decade, the amount of data, particularly images, that has to be processed in a variety of applications has increased significantly, and thus the usage of effective and fast filtering algorithms has become crucial. The aim of the present research is to identify what type of software (MATLAB, Scilab, or MPI-based) is preferred for reducing the filtering time and consequently save energy. Thus, the aim of the present research corresponds to actual trends in information processing and corresponds to green computing concepts. A set of experimental images divided into two groups—one for small images and a second one for big images—is used for performance evaluation of the recursive mean filter. This type of linear filter was chosen due to its very good denoising characteristics. The filter is implemented in MATLAB and Scilab environments using their specific commands and it is also implemented using the C language with the MPI library to provide the opportunity for parallel execution. Two mobile computer systems are used for experimental performance evaluation and the results indicate that the slowest filtering execution is registered when Scilab is used and the fastest execution is achieved when MPI is used with the C implementation. Depending on the amount and size of the images that have to be filtered, this study formulates advice for achieving effective performance throughout the whole process of working with images.

Keywords

• digital image processing
• Scilab
• MATLAB
• MPI
• multi-core architectures