Applications of multiwavelet techniques to image denoising

The developments in wavelet theory have given rise to the wavelet thresholding method, for extracting a signal from noisy data [1,2]. Multiwavelets, wavelets with several scaling functions, have recently been introduced and they offer simultaneous orthogonality, symmetry and short support; which is not possible with ordinary wavelets, also called scalar wavelets [3]. This property makes multiwavelets more suitable for various signal processing applications, especially compression and denoising. Like scalar wavelets, multiwavelets can be realized as filterbanks, however the filterbanks are now matrix-valued; requiring two or more input streams, which can be accomplished by prefiltering. In this paper, several thresholding methods to be used with different multiwavelets for image denoising are presented. The performances of multiwavelets are compared with those of scalar wavelets. Simulations reveal that multiwavelet based image denoising schemes outperform wavelet based methods both subjectively and objectively.