Cellular automata-based efficient method for the removal of high-density impulsive noise from digital images

Abstract

Cellular automata (CA) are simple dynamical systems used for solving simple as well as complex problems. It uses a discrete space, like the space used in spatial domain for image processing, to store the values of the input data. Then, the input data can be processed according to the transition rules, functions, or, programmes, for the required output. This research work presents an efficient algorithm based on two dimensional cellular automata (2D CA), with hybrid rules under null and periodic boundary conditions, for filtering high-density impulsive noise from corrupted digital images. The most important advantage of the proposed method is that, it can be applied on all types of digital images (binary, greyscale, or, true color). The paper is organized as follows: Sect. 1 gives a brief introduction towards the problem of noise in digital images with emphasis on its solution. Further, this section presents a brief review of existing standard noise filtering methods based on general image processing and CA techniques. Section 2 discusses the basic concept of CA with special emphasis on 2D CA and related concepts. Section 3 presents the proposed algorithm for the removal of high-density impulsive noise from corrupted digital images. Section 4 discusses, in detail, the experimental results using both mathematical and subjective analysis; and, the extensive experimentation reveals that the proposed 2D CA based algorithm yields better results than the standard noise filtering algorithms. And, then finally Sect. 5 presents the conclusions and future scope.