The wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise. However, the conventional wavelet shrinkage based methods are not time-scale adaptive to track the local time-scale variation. In this paper, a new time-scale adaptive denoising method for deterministic signal estimation is presented, based on the wavelet shrinkage. A class of smoo...

In recent years wavelet have had an important impact on signal processing theory and practice. The eeectiveness of wavelets is mainly due to their capability of representing piecewise smooth signals with few non-zero coeecients. Away from discontinuities, the inner product between a wavelet (with a number of zero moments) and a smooth function will be either zero or very small. 8 At singular po...

This paper presents a computer based manipulating and analyzing a digital images. The proposed method is used wavelet transform, this transformation arrange orthogonal series of both imaginary and real values. In this paper we are proposes a two algorithms one is dual tree complex wavelet transforms (DTCWT), and second one is dual tree complex wavelet transform with orthogonal shift property th...

Removal of noise is an essential step in the preprocessing of microarray images for obtaining betterquality gene expression measurements. Wavelet-based methods for denoising of images are very successful. However, for cDNA microarray images, existing methods are not as efficient because they fail to take into account linear dependencies that exist between wavelet coefficients of the red and gre...

Wavelet transform is a one of the most powerful concept used in image processing. Wavelet transform can divide a given function into different scale components and can find out frequency information without losing temporal information. Wavelet Transform is more suitable technique as compared to fourier transform because it is not possible with fourier transform to observe varying frequencies wi...

There are many unavoidable noise interferences in image capturing and transmission. In order to make it better for subsequent processing, the noise in the image should be removed in advance. There are many kinds of image noises, mainly including salt & pepper noise and Gaussian noise. This paper focuses on the research of the Gaussian noise removal. It introduces many wavelet threshold denoisin...

Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. But the choice of thresholding function has restricted there wide spread use in image denoising application. In this paper we proposed a computationally more efficient thresholding scheme by incorporating the neighbouring wavelet coefficients, with different threshold ...

A Translation Invariance Denoising Algorithm with Wavelet Threshold and its Application on Signal Processing of Laser Interferometer Hydrophone is investigated. The obtained signal of Laser interferometer hydrophone exist a large number of singularity points, and the denoising algorithm of Donoho’s wavelet threshold may produce the Pseudo Gibbs phenomenon on the singularity points. To eliminate...

Wavelet transforms have been utilised effectively for image denoising, providing a means to exploit the relationships between coefficients at multiple scales. In this paper, a modified structure is presented that enables the utilisation of an unlimited number of wavelet filters. An alternative denoising technique is thus proposed with a simple approach for the utilisation of multiple wavelet fi...

Non-stationary signal processing applications use standard nonredundant DWT (Discrete Wavelet Transform) which is very powerful tool. But it suffers from shift sensitivity, absence of phase information, and poor directionality. To remove out these limitations, many researchers developed extensions to the standard DWT such as WP (Wavelet Packet Transform), and SWT (Stationary Wavelet Transform)....