Abstract:
Wavelet based image processing techniques do not strictly follow the conventional probabilistic models that are unrealistic for real world images. However, the key features of joint probability distributions of wavelet coefficients are well captured by Hidden Markov Tree model.
This thesis presents Hidden Markov Tree model based technique consisting of Wavelet based Multiresolution analysis to enhance the results in image processing applications such as compression, classification and denoising. The proposed technique is applied to colored video sequences by implementing the algorithm on each video frame independently. A 2D-Discrete Wavelet Transform is used which is implemented on popular Hidden Markov Tree Model used in the framework of Expectation Maximization algorithm. The proposed technique can properly exploit the temporal dependencies of wavelet coefficients and their non-Gaussian performance as opposed to existing wavelet based denoising techniques which consider the wavelet coefficients to be jointly Gaussian or independent.
Denoised frames are obtained by processing the wavelet coefficients inversely. Detailed comparison has been made with the existing state of the art techniques.The proposed denoising method reveals improved results in terms of quantitative and qualitative analysis for both additive and multiplicative noise and retains nearly all the structural contents of a video frames.
