Search results for "Biorthogonal wavelet"
showing 6 items of 6 documents
Applications of Harten’s Framework for Multiresolution: From Conservation Laws to Image Compression
2002
We briefly review Harten’s framework for multiresolution decompositions and describe two situations in which two different instances of the general framework have been used with success.
Optimal extension of multispectral image demosaicking algorithms for setting up a one-shot camera video acquisition system
2022
Multispectral images are acquired using multispectral cameras equipped with CCD or CMOS sensors which sample the visible or near infrared spectrum according to specific spectral bands. A mosaic of multispectral MSFA filters is superimposed on the surface of the sensors to acquire a raw image called an MSFA image. In the MSFA image, only one spectral band is available per pixel, the demosaicking process is necessary to estimate the multispectral image at full spatio-spectral resolution. Motivated by the success of single-sensor cameras capturing the image in a single exposure that use CFA filters, we performed a comparative study of a few recent color image demosaicking algorithms and experi…
Biorthogonal Wavelet Transforms
2015
Wavelets in the polynomial and discrete spline spaces were introduced in Chaps. 8 and 10, respectively. In both cases, the wavelets’ design and implementation of the transforms were associated with perfect reconstruction (PR) filter banks. In this chapter, those associations are discussed in more detail. Biorthogonal wavelet bases generated by PR filter banks are investigated and a few examples of compactly supported biorthogonal wavelets are presented. Conditions for filters to restore and annihilate sampled polynomials are established (discrete vanishing moment property). In a sense, the material of this chapter is introductory to Chap. 12, where splines are used as a source for (non-spli…
An improved MSD-based method for PD pattern recognition
2007
In this paper a new method for multi source partial discharge (PD) pattern recognition by discrete wavelet transform, is presented. The proposed method is based on the application of multi-resolution signal decomposition (MSD) technique applied to a 3D PD pattern image. The multi-resolution technique has shown that some detail images (horizontal, vertical and diagonal) at different decomposition levels have frequencial characteristics typical of a single discharge phenomenon. By applying this method to a double-source PD pattern it is observed that one source prevails on the other. Taking advantage from the linearity of the biorthogonal wavelet functions by using a removal and reconstructio…
Biorthogonal Wavelet Transforms Originating from Discrete and Discrete-Time Splines
2018
This chapter describes how to generate families of biorthogonal wavelet transforms in spaces of periodic signals using prediction p-filters originating from discrete-time and discrete splines. The transforms are generated by the lifting scheme (Sweldens (Wavelet applications in signal and image processing III, vol 2569, 1995, [7]), Sweldens (Appl Comput Harmon Anal 3:186–200, 1996, [8]), Sweldens (SIAM J Math Anal 29:511–546, 1997, [9]), see also Sect. 7.1 of this volume). The discrete-time wavelets related to those transforms are (anti)symmetric, well localized in time domain and have flat spectra. These families comprise wavelets with any number of local discrete vanishing moments (LDVMs)…
Biorthogonal Wavelet Transforms Originating from Splines
2015
This chapter describes how to design families of biorthogonal wavelet transforms of signals and respective biorthogonal Wavelet bases in the signal space using spline-based prediction filters. Although the designed Wavelets originate from splines, they are not splines themselves. The design and implementation of the biorthogonal Wavelet transforms is done using the Lifting scheme. Most of the filters participating in the expansion of signals over the presented bases have infinite impulse responses and are implemented by recursive filtering whose computational cost is competitive with the FIR filtering cost. Properties of the designed Wavelets, such as symmetry, flat spectra, good time domai…