Hidden Markov Model Based Image De-Noising
Mrs.Seema Deoghare1, Aditya Satvekar2, Nitin Bhoye2 and Chintan Shah2
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In the image processing technology, noise is the major part which degrades the quality of Image. Noise will create an error in the image, So we have to make such a system which reduces the noise or eliminate the noise. In digital image several types of noise are present. For elimination of these noise we requires a filter. For different noise we have to use different filters. But the problem will arises when a image contain lots of noise present (Ex: Salt & pepper noise, Gaussian noise) then we can’t use these filters. So we have to make a universal filter/Algorithm which de-noises the image. In our propose system we are using “M-Universal Hidden Markov Tree” algorithm we propose a new image de-noising algorithm, called M-uHMT. It is simple and effective. Simulation results show that the proposed M-uHMT can achieve the state-of-the-art image de-noising performance at the low computational complexity. The proposed algorithm has two major steps: an optimum estimation of the wavelet coefficients based on the uHMT model and an averaging of the de-noised images. Each step contributes to improvement in de-noising performance.