Cellular Automata Based Identification and Removal of Impulsive Noise from Corrupted Images

Cellular Automata (CA) is a methodology that uses discrete space to represent the state of each element of a domain and this state can be changed according to a transition rule. Image noise is unwanted information of an image. Noise can occur during image capture, transmission or processing and it may depend or may not depend on image content. Noise reduction is one of the important processes in the pre-processing of digital images. Most primitive approaches used neighbour pixel values to replacement of noisy pixels. But these methods have a big disadvantage that they are applied on all the pixels, corrupted as well as un-corrupted pixels. So the images loosed vital texture such as edges. Recently researchers have been proposed classification based methods, in this case first identify the corrupted pixel and then replace it by the neighbour values whereas uncorrupted pixels remain unchanged. The proposed method first identifies the noise and then removes it from the corrupted image based on CA. To illustrate the proposed method, some experiments have been performed on several standard test images and compared with popular methods of filtering. The results show that the proposed method relatively has the desirable performance visibly as well as. First the concept of CA is introduced, and then accordingly to the structure of the neighbors, proposed model and then the experimental results.