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...

every image captured on a screen has possibilities of having noise, which is undesirable. With the introduction of noise in an image causes distortions in quality of the image. Hence it is important to eliminate noise from an image to retain the original information in the image. There are many types of noise which can be found associated with the captured image. This paper discusses about a fe...

In this paper a novel approach for de noising images corrupted by random valued impulses has been proposed. Noise suppression is done in two steps. The detection of noisy pixels is done using all neighbor directional weighted pixels (ANDWP) in the 5 x 5 window. The filtering scheme is based on minimum variance of the four directional pixels. In this approach, relatively recent category of stoch...

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...

In the paper a new orthorectification strategy of aerial images is presented. The strategy is focused on improvement of visual quality of orthoimage. The idea of proposed strategy relies on extraction of edges from the image and next special resampling is taken. The critical issue for edge detection is that the images usually includes some noise. The de-noising of an image has many solutions bu...

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...

Soft thresholding has been a standard wavelet de-noising procedure in many signal and image processing applications. Theoretically, it is also almost optimal in the sense of nearly achieving the minimax mean-squared error. Inspired by this property, this paper proposes the addition of coefficient de-noising before soft thresholding. This extra step serves to reduce noise in the empirical wavele...