ITERATIVE CELLULAR IMAGE PROCESSING ALGORITHM

In this paper, a new iterative image processing algorithm is introduced and denoted as “iterative cellular image processing algorithm” (ICIPA).  The new unsupervised iterative algorithm uses the advantage of stochastic properties and neighborhood relations between the cells of the input image.  In ICIPA scheme; first regarding to the stochastic properties of the data, all possible quantization levels are determined and then 2D input image is processed using a function, based on averaging and neighborhood relationship, and after that a parameter C is assigned to each cell.  Then Gaussian probability values are mapped to each cell regarding to all possible quantization levels and the attended value C.  A maximum selector defines the highest probability value for each cell.  In the case of complex data, first iteration output is fed into input till a sufficient output is found.     We have applied ICIPA algorithm to various synthetic examples and then a real data, the ruins of Hittite Empire. Satisfactory results are obtained. We have observed that de-noising property of our scheme is the best in the literature.  It is interesting that the corrupted data with Additive White Gaussian Noise (AWGN) up to 97% ratio, can be de-noised by using our proposed ICIPA algorithm.