Image Processing Method Based On Cellular Automata Transform

Cellular Automata Transforms present a more direct way of achieving the linkage between a given phenomenon and the evolving CA field. It maps each point on the physical space into cellular automata domain using the basis functions of CA. The transform coefficients in CA filed reveal features not observed in the physical domain. The huge number of transform bases available and varied nature is a major strength of these CA transform. These can be adapted to the peculiarities of a given problem and provided an excellent platform for image compression, data encryption and solution of integral equations.The paper briefly introduces the basic theory and the development of cellular automata transform and we apply it in the image processing. Firstly, we applied the cellular automata transform to image classification and then the description of intrinsic characteristics, which is achieved by using the appropriate orthogonal bases and energy value presented by the cellular automata transform coefficients, lead to the success in image classification based on the physical feature of image. Secondly, we attempted to apply the cellular automata transform to identifying the type of image noise. Using the appropriate orthogonal bases and energy distribution presented by the high frequency components of cellular automata transform, lead to the success in discriminating Gaussian and the Salt & Pepper noise.Otherwise, we compared the capability of cellular automata transform with that of discrete cosine transform, wavelet transform and walsh-hadamard transform in energy compaction efficiency aspect. Then compressing image and comparing the reconstruction image's MSE value used the discrete cosine transform, wavelet transform, walsh-hadamard transform and cellular automata transform.