A New Image Denoising Method via Self-Organizing Feature Map Based on Hidden Markov Models

The Wavelet-domain hidden Markov Models (HMMs) can powerfully preserve the image edge information, but it lacks local dependency information. According to the deficiency, a novel image denoising method based HMMs via the self-organizing feature map (SOFM) which exploits spatial local correlation among image neighbouring wavelet coefficients is proposed in this paper. SOFM algorithms is popular for unsupervised learning, data clustering and data visualization, and it can capture persistence properties of wavelet coefficients. Experimental results show that the performance of the proposed method is more practicable and more effective to suppress additive white Gaussian noise and preserve the details of the image.