Enhanced computation method of topological smoothing on shared memory parallel machines

TL;DR

This paper introduces an enhanced parallel computation method for topological smoothing of 2D binary images that preserves topology, improves speed, and manages memory efficiently on shared memory parallel machines.

Contribution

It proposes a novel parallelization strategy called SDM for topological operators, ensuring topology preservation and better resource management in image smoothing.

Findings

Achieved 5.2x speedup on a 512x512 image using 8 CPU cores.

Maintained topology with a success rate of 70% during smoothing.

Demonstrated improved memory management and parallel efficiency.

Abstract

To prepare images for better segmentation, we need preprocessing applications, such as smoothing, to reduce noise. In this paper, we present an enhanced computation method for smoothing 2D object in binary case. Unlike existing approaches, proposed method provides a parallel computation and better memory management, while preserving the topology (number of connected components) of the original image by using homotopic transformations defined in the framework of digital topology. We introduce an adapted parallelization strategy called split, distribute and merge (SDM) strategy which allows efficient parallelization of a large class of topological operators. To achieve a good speedup and better memory allocation, we cared about task scheduling and managing. Distributed work during smoothing process is done by a variable number of threads. Tests on 2D grayscale image (512*512), using shared memory parallel machine (SMPM) with 8 CPU cores (2 Xeon E5405 running at frequency of 2 GHz), showed an enhancement of 5.2 with cache success rate of 70%.