Quadrature mirror filter

About: Quadrature mirror filter is a research topic. Over the lifetime, 955 publications have been published within this topic receiving 28900 citations.

Multiresolution image decomposition for processing reconnaissance images

Abstract: Recent increases in focal plane size and spatial resolution, coupled with the introduction of multi-spectral sensor suites in tactical reconnaissance platforms, have exponentially increased the volume of image data to be processed. This paper proposes the use of a quadrature mirror filter compression technique whereby intrinsic multiresolution pyramid decomposition can simplify implementation of many of the algorithms required to exploit these images. A subband coding system, based on the spatial frequency and orientation of the decomposed images, is presented. Implementation of additional algorithms based on multiresolution pyramid decomposition is also discussed. These algorithms include: edge detection and restoration, real-time minification of large E-O images for screening purposes and interpolation with electronic zooming for target identification.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The Detection and Extraction of Features of Low Probability of Intercept Signals Using Quadrature Mirror Filter Bank Trees.

Abstract: : A new type of spread spectrum intercept receiver is described which uses orthogonal Wavelet techniques and a Quadrature Mirror Filter (QMF) bank tree to decompose a waveform into components representing the energy in rectangular "tiles" in the time frequency plane By simultaneously examining multiple layers of the tree, the dimensions of concentrations of energy can be estimated with a higher resolution than is normally associated with linear transform techniques This allows detection and feature extraction even when the interceptor has little knowledge of specific parameters of the signal being detected In addition, the receiver can intercept and distinguish between multiple signals For each category of spread spectrum, the receiver estimates the energy cells' positions in the time frequency plane, the cells' bandwidths, time widths and signal to noise ratios, and the energy distribution within each cell With this information, a classifier can then determine how many transmitters there are, and which cells belong to each In this report, algorithms are described for detecting and extracting features for each of the spread spectrum signal formats These algorithms are analyzed mathematically and the results are verified with simulation The detection abilities of these algorithms are compared with other spread spectrum detectors hat have been described in the literature

FPGA-Based Quadrature Mirror Filters for DSP Applications

Abstract: One of the most efficient multi-rate digital signal decomposition structures is the quadrature mirror filter (QMF) bank.It has been used successfully to implement subband coding applications such as speech coding and image compression. In this paper, we report on several efficient FPGA-based implementations of the basic quadrature mirror filter bank. The implementations are based on transforming the basic filter bank structure using ployphase decomposition and distributed arithmetic techniques. The two techniques reduce computational time dramatically by enabling maximum exploitation of the ample parallelism inherent in the filter bank. Performance results demonstrate the effectiveness of FPGA implementations of ployphase and distributed arithmetic quadrature mirror filter bank.

Impedance matrix compression using an effective quadrature filter

Abstract: An effective quadrature mirror filter (QMF) proposed by Vaidyanathan has been used to solve 2D scattering problems. QMF has been popular for some time in digital signal processing, under the names of multirate sampling, wavelets, etc. In this work, the impulse response coefficients of QMF were used to construct the wavelet transform matrix. Using the matrix to transform the impedance matrices of 2D scatterers produces highly sparse moment matrices that can be solved efficiently. Such a presentation provides better sparsity than the celebrated and widely used Daubechies wavelets. These QMF coefficients are dependent on the filter parameters such as transition bandwidth and filter length. It was found that the sharper the transition bandwidth, the greater the reduction in nonzero elements of the impedance matrix. It also can be applied in the wavelet packet algorithm to further sparsify the impedance matrix. Numerical examples are given to demonstrate the effectiveness and validity of our finding.