International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7, No.4 (2014), pp.231-242 http://dx.doi.org/10.14257/ijsip.2014.7.4.23 ISSN: 2005-4254 IJSIP Copyright ⓒ 2014 SERSC An Enhanced Cellular Automata based Scheme for Noise Filtering Anand Prakash Shukla 1 and Suneeta Agarwal 2 1 Krishna Institute of Enginering and Technoloty, Ghaziabad, India 2 Motilal National Institute of Technology, Allahabad, India. anandskla@gmail.com, suneeta@mnnit.ac.in Abstract Cellular Automata is a computational model used to describe the complex system through simple rules. It has been significantly applied to image processing operations. It appear as a natural tool for image processing because of the simplicity of mapping a digital image to a cellular automata and the ability of applying different image processing operations in real time. Noise filtering is considered to be an important operation of image processing. In this paper cellular automata based noise filter has been proposed for different levels of noise. The filter is also compared with some standard filters in terms of peak signal to noise ratio and structured symmetry index measure and it is found that the proposed model shows consistently better performance in terms of both the parameter. Keywords-Cellular automata, Noise filter, Gaussian noise, Salt & pepper noise, Moore Neighborhood, PSNR, MSSIM 1. Introduction Due to simple structure of cellular automata (CA) to model complex behavior system, it has attracted various researchers from different areas. Cellular automata primarily announced by Ulam[1] and Von Neumann [2] in 1950’s and also discussed in the book of Wolfram ’A New Kind of Science’[3] with the purpose of obtaining models of biological self-reproduction. Nowadays cellular automata became very popular because of its diverse function and utility as a discrete model for many processes. Cellular automata also provide a concept for computational automata. Cellular automata also called Systems of Finite Automata i.e., Deterministic Finite Automata (DFA) arranged in an infinite, regular lattice structure[4]. In cellular automata state of a cell at the next time step is determined by the current states of a surrounding neighborhood of cells along with its own state and is updated synchronously in discrete time steps. Cellular automaton is a discrete dynamical system. Space, time, and the states of the system are discrete. Each point in a regular spatial lattice, called a cell, can have any one of a finite number of states. The states of the cells in the lattice are updated according to a local rule. That is, the state of a cell at a given time depends only on its own state one time step previously, and the states of its nearby neighbors at the previous time step. All cells on the lattice are updated synchronously. Thus the state of the entire lattice advances in discrete time steps. Formally, a (bi-directional, deterministic) cellular automaton is a triplet A = (S;N; δ), Where, S is an non-empty state set, N is the neighborhood system, and δ: S N → S is the local transition function (rule).This function defines the rule of calculating the cell’s state at t +1 time step, given the states of the neighborhood cells at previous time step t. Online Version Only. Book made by this file is ILLEGAL.