What are DSP wavelets?
Answers from top 17 papers
More filters
| Papers (17) | Insight |
|---|---|
21 Citations | Although the wavelets have been used widely in the context of image fusion they are not working well with DSA and mask image fusion. |
| The same approach can be applied with other signals where the embedded implementation of wavelets can be beneficial. | |
27 Citations | Simulation results show that the designed wavelets are effective and more efficient than the existing standard wavelets. |
27 Citations | These wavelets are well-suited to identifying the forward and reverse components. |
01 Jan 2005 | Wavelets have shown to provide suitable signal |
| Regardless of whether the function of interest is an image, a curve, or a surface, wavelets offer an elegant technique for representing the levels of detail present. | |
| Warped wavelets have been introduced recently, they offer very good computable and easy-to-implement properties while being well adapted to the statistical problem at hand. | |
| The inherent properties of wavelets makes it useful in image denoising, edge detection, image compression, compressed sensing and illumination normalization. | |
28 Sep 1999 29 Citations | The motivation behind this selection of wavelets is that Daubechies wavelets lead to more accurate results by better matching the self-similar structure of long-range dependent processes, than other types of wavelets. |
| This presentation can be regarded as an introduction to “wavelets” and this material is, for the most part, self-contained. | |
02 Nov 1997 | Therefore, these new wavelets are promising in a broad range of applications in signal processing and numerical analysis. |
24 Citations | By relieving the orthogonality and enforcing normalization, the wavelets are locally supported and admissible, hence facilitating data analysis and geometry processing. |
| It is shown that higher multiplicity wavelets adapted to this task are clearly superior to wavelets packets using Daubechies wavelets. | |
| Unfortunately, it appears that the quantitative properties of these wavelets are rather disappointing. | |
26 Citations | The present study shows that wavelets are a powerful tool to overcome this drawback. |
| In addition, the experiments show that wavelet shrinkage de-noising based on DSP is satisfying. | |
| The added flexibility opens the possibility to create new families of wavelets which are specially suitable for analog implementations. |
Related Questions
What are the advantages of the wavelet transform over the Fourier transform?4 answersThe wavelet transform has several advantages over the Fourier transform. Firstly, the wavelet transform provides a multi-resolution analysis, allowing for the examination of signals at different scales or frequencies. This is in contrast to the Fourier transform, which only provides a global frequency analysis. Secondly, the wavelet transform has a constant relative bandwidth, meaning that it can accurately represent both high and low frequency components of a signal. In comparison, the Fourier transform has a fixed frequency resolution, which may not capture all the details of a signal. Additionally, the wavelet transform is able to represent local characteristics of signals in both the time and frequency domains, making it a powerful tool for signal analysis. These advantages make the wavelet transform suitable for a wide range of applications, including multimedia processing, signal denoising, and wireless communications.
What are the different families of wavelets?5 answersWavelets are mathematical tools used to decompose and represent functions in the time domain. Different families of wavelets have been developed for various applications. The Daubechies wavelet family, Coiflet wavelet family, Morlet wavelet family, and biorthogonal wavelet family are discussed in. These families have different properties and are used in different contexts. In the study of Molecular Dynamics (MD) trajectories, wavelets have been used to select conformations from MD trajectories, and the OWSCA method has been developed using wavelets. In image denoising, wavelet families such as Haar, Symlets, Coiflets, Daubechies, Meyer, and Biorthogonal have been compared and analyzed. New families of wavelets have also been constructed, such as frames suited for frequency localized signals and frames suited for time localized signals. In EEG signal processing, different wavelet families including Haar, Coiflets, Biorthogonal, Reverse Biorthogonal, and Daubechies have been compared for feature extraction.
What are the applications of Wavelet Neural Networks (WNNs) in image ?4 answersWavelet Neural Networks (WNNs) have various applications in image classification and feature learning. One application is the use of WNNs for data stream classification and anomaly detection in off-line and online environments, where the length of data is unconstrained. Another application is the improvement of classification accuracy in image classification tasks. This is achieved by using an add-wavelet transform block (AWT) and a novel adder neural network (AddWaveNets) that incorporates the AWT, resulting in significant improvements in classification accuracy and feature learning compared to state-of-the-art quantization networks. Additionally, WNNs can be integrated into existing convolutional neural networks (CNNs) by replacing the pooling operation. This integration allows for full utilization of different frequency components and enhances consistency and accuracy in image classification.
How do you create a wavelet in Python?9 answers
What are the Matlab functions that are available for the wavelet operation on images?8 answers
What is wavelets and multi resolution processing?8 answers