Two-dimensional patterns and images reconstruction with use of cellular automata

Anna Piwonska,Jaroslaw Skaruz,Franciszek Seredynski

doi:10.1007/s11227-014-1214-x

Abstract

An approach based on an application of cellular automata (CA) to the problem of two-dimensional (2D) patterns or images reconstruction from ones with only partial information available is presented in the paper. 2D CA are used to process patterns/images, and genetic algorithm (GA) is applied to discover CA rules, which will be able to reconstruct original patterns/images from, e.g. destroyed or modified ones. A number of experiments have been conducted to reconstruct patterns and human face images with use of the proposed approach. Results of experiments show that CA rules discovered by GA in the learning process allow to reconstruct images with large number of damaged pixels.

Highlights

An object reconstruction problem belongs to a wide area of image processing which is important in numerous applications, such as medicine, acquisition of sensor data, pattern recognition, etc
In this paper we propose a genetic algorithm (GA)-based approach to discover cellular automata (CA) rules to perform object reconstruction tasks: pattern and human face image reconstruction
Results of experiments showed that GA needed around 30 generations to discover CA rules with a good performance, despite the value of p

Summary

Introduction

An object (pattern, image) reconstruction problem belongs to a wide area of image processing which is important in numerous applications, such as medicine, acquisition of sensor data, pattern recognition, etc. In [9] an approach based on CA to noise filtering and thinning of binary images has been proposed. Authors of [8] applied CA to solve noise removal and border detection in images. During the 1990s, Mitchell and colleagues [6] proposed using GAs to discover CA rules, which are able to solve density classification problems. It opened a possibility of automatic generation of CA rules using artificial evolution. In this paper we propose a GA-based approach to discover CA rules to perform object reconstruction tasks: pattern and human face image reconstruction.

Cellular automata overview

An object reconstruction problem and cellular automata

Coding solution for GA-based search

Learning phase

Testing phase

Experiment 1: reconstructing patterns

Experiment 2: reconstructing human face images

Conclusions