Multiwavelet Filter Banks Research Articles

Automated Lung Tissue Segmentation in CT Images using Multi-Wavelet Filter Banks and Random Forest Algorithm

Automated Lung Tissue Segmentation in CT Images using Multi-Wavelet Filter Banks and Random Forest Algorithm

ON PRECONDITIONING MULTIWAVELET SYSTEMS FOR IMAGE COMPRESSION

This paper presents a study on applications of multiwavelet analysis to image compression. The biorthogonality and perfect reconstruction conditions are applied to multiwavelet filters. As a multiwavelet filter bank has multiple channels of inputs, the data structure of inputs to the multiwavelet system should be taken into consideration in multiwavelet decomposition and reconstruction algorithms. We investigate the data initialization problem by considering prefilters and postfilters that may give more efficient representations of the decomposed data. The interpolation postfilter and prefilter are formulated, which are capable to provide a better approximate image at each coarser resolution level. A design process is given to obtain both filters having compact supports, if exist. Image compression performances of three multiwavelet systems are studied in comparison to those of single wavelet systems.

The application of multiwavelet transform to image coding

This work presents a new image coding scheme based on multiwavelet filter banks. First, two dimensional (2-D) multiwavelet decomposition is performed on the original image. Then, several hierarchical trees are constructed in the transform domain, and an extension of set partitioning in hierarchical trees algorithm is proposed to quantize multiwavelet coefficients. Our simulation shows that this scheme is effective and promising.

Factorisation for subclass of symmetric-antisymmetric multifilter banks and its application to image coding

A complete and minimal factorisation is proposed for an important subclass of symmetric-antisymmetric multiwavelet filter banks (SAMFBs). Based on this factorisation, optimal SAMFBs are designed for image coding under an optimisation criterion. Experimental results show that the SAMFBs significantly outperform the traditional 9-7 wavelet in terms of coding efficiency, especially for hard-to-compress images.

Multiwavelet bases with extra approximation properties

This paper highlights the differences between traditional wavelet and multiwavelet bases with equal approximation order. Because multiwavelet bases normally lack important properties that traditional wavelet bases (of equal approximation order) possess, the associated discrete multiwavelet transform is less useful for signal processing unless it is preceded by a preprocessing step (prefiltering). This paper examines the properties and design of orthogonal multiwavelet bases, with approximation order >1 that possess those properties that are normally absent. For these bases (so named by Lebrun and Vetterli (see Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Munich, Germany, vol.3, p.2473-76, 1997 and ibid., vol.46, p.1119-24, 1998)), prefiltering can be avoided. By reorganizing the multiwavelet filter bank, the development in this paper draws from results regarding the approximation order of M-band wavelet bases. The main result thereby obtained is a characterization of balanced multiwavelet bases in terms of the divisibility of certain transfer functions by powers of (z/sup -2r/-1)/(z/sup -1/-1). For traditional wavelets (r=1), this specializes to the usual factor (z+1)/sup K/.