This is a short summary of a talk given at the Frontier Science in EEG Symposium, Continuous Waveform Analysis, held on 9 October 1993 in New Orleans. We describe some new libraries of waveforms well-adapted to various numerical analysis and signal processing tasks. The main point is that by expanding a signal in a library of waveforms which are well-localized in both time and frequency, one ca...

Sparse representation techniques have become an important tool in image processing in recent years, for coding, de-noising and in-painting purposes, for instance. They generally rely on an penalized criterion and fast algorithms have been proposed to speed up the applications. We propose to replace the -part of the criterion, which has been chosen both for its easy implementation and its relati...

The local adaptive processing of signals and images in a transform domain within a sliding window suggests certain advantages in some signal and image de-noising applications due to incorporating an available a priori information about the signals and noises. However, an optimum transform size is also data dependent and generally is not known in advance. Performing the de-noising with the varyi...

The research area of image processing technique using fuzzy k-means and wavelet transform. The enormous amount of data necessary for images is a main reason for the growth of many areas within the research field of computer imaging such as image processing and compression. In order to get this in requisites of the concerned research work, wavelet transforms and k-means clustering is applied. Th...

Segmentation of adjoining objects in a noisy image is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. Segmentation of these noisy images does not provide desired results, hence de-noising is required. In this paper, we tried to address a very effective technique called Wavelet thresholding for denoising, as it can arrest t...

A two stage algorithm is presented in this paper to design optimal M-band orthonormal wavelets of compact support for signal de-noising. A cost function d(.,.). suitable for the signal de-noising, is minimized to select the optimal basis. A parameterized representation of wavelet bases is used to constrain the values and reduce the number of independent parameters with the freedom of choice of ...

Through the analyzing of limitations on wavelet threshold filter de-noising, this paper applies wavelet filter based on compressed sensing to reduce the signal noise of spectral signals, and compares the two methods through experiments. The results of experiments shown that the wavelet filter based on compressed sensing can effectively reduce the signal noise of spectral signal. The de-noising ...

ABSTRACT Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis are called principal components. Since Kernel PCA is just a PCA in feature space F , the projection of an image in input space can be reconstructed from its principal components in feature space. This enables us...

The paper presents real time speckle de-noising based on activity computation algorithm and wavelet transform. Speckles arise in an image when laser light is reflected from an illuminated surface. The process involves detection of speckles in an image by obtaining a number of frames of the same object under different illumination or angle and comparing the frames for the granular computation an...

In the threshold de-noising method based on wavelet transform, not only the threshold and threshold function, but also the decomposition level is an important factor in practical application. Signals under different noise levels correspond with different optimal decomposition levels. A method to determine the optimal decomposition level based on the white noise verification of wavelet detail co...