In the fluorescence detection system, noise will affect the sensitivity of the system. The method using Stationary Wavelet Transform to de-noise fluorescence signals based on adaptive wavelet entropy is studied. The de-noising effects of dbN families are compared, and then the db7 wavelet is chosen as the optimal wavelet. The noised fluorescence signal is decomposed at 5 levels via Discrete Wav...

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 natural frequency and scanning vibration frequency of C-dynamic scanning system of laser sensors are acquired for film thickness of lithium battery de-noising based on multi-resolution wavelet algorithm. For this reason, fast de-noising based on atomic sequence template library is present. First, under various mode of scanning, best atomic sequence template is built by sparse decomposition....

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...

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...

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 ...

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...

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 wavelet de-noising technique with wavelet based function has been used in this paper for bearing fault detection. The applications of the wavelet de-noising show that the fault pulses in time-domain of the de-noised signals are easily to be detected as a result of removing the covering noise, which is not possible through the time-domain analysis of the original signal. Furthermore, the rec...