seismic waves are non-stationary due to its propagation through the earth. time-frequency transformsare suitable tools for analyzing non-stationary seismic signals. spectral decomposition can reveal thenon-stationary characteristics which cannot be easily observed in the time or frequency representationalone. various types of spectral decomposition methods have been introduced by some researche...

Translation invariant (TI) single wavelet de-noising was developed by Coifman and Donoho and they show that TI is better than non-TI single wavelet de-noising. On the other hand, Strela et al. have found that non-TI multiwavelet de-noising gives better results than non-TI single wavelets. In this paper we extend Coifman and Donoho's TI single wavelet de-noising scheme to multiwavelets. Experime...

introduction: spect is a diagnostic imaging technique the main disadvantage of which is the existence of poisson noise. so far, different methods have been used by scientists to improve spect images. the wavelet transform is a new method for de-noising which is widely used for noise reduction and quality enhancement of images. the purpose of this paper is evaluation of noise reduction in spect ...

De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) ...

Because the classic intersecting cortical model (ICM) and the traditional image de-noising algorithm exist the deficiencies-the image collection, transmission and conversion are often subjected to impulse noise interference, thus affecting the quality of the image, therefore we improved the framework structure and related parameters of the ICM and proposed the adaptive image de-noising algorith...

In digital image different kinds of noises exist in an image and a variety of noise reduction techniques are available to perform de-noising. Selection of the de-noising algorithm depends on the types of noise. Gaussian noise, speckle noise, salt & pepper noise, shot noise are types of noises that are present in an image. The principle approach of image de-noising is filtering. Available filter...

This paper briefly describes the basic principle of wavelet packet analysis, and on this basis introduces the general principle of wavelet packet transformation for signal den-noising. The dynamic EEG data under +Gz acceleration is made a de-noising treatment by using wavelet packet transformation, and the de-noising effects with different thresholds are made a comparison. The study verifies th...

Signal de-noising is one of the classical problems in the field of signal processing. As a new signal processing tools, wavelet analysis, which has excellent noise performance, has caused growing concern and attention. The wavelet threshold de-noising has been researched systematacially, and the wavelet de-noising method is used on the GPS signal, which has achieved very good results.

Patch-based de-noising algorithms and patch manifold smoothing have emerged as efficient de-noising methods. This paper provides a new insight on these methods, such as the Non Local Means [1] or the image graph de-noising [8], by showing its use for filtering a selected pattern. K ̄ eywords: NL-Means, diffusion processes, diffusion geometry, graph filtering, patch manifold.