抽象的

Hidden Markov Model Based Image De-Noising

Mrs.Seema Deoghare , Aditya Satvekar , Nitin Bhoye , Chintan Shah

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.