Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain

Background: Array-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy number changes is important for both the basic understanding of cancer and its diagnosis. In order to develop effective methods to identify aberration regions from array CGH data, many recent research work focus on both smoothing-based and segmentation-based data processing. In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH data. Our purpose is to remove CGH noise in whole frequency while keeping true signal by using bivariate model. Results: In both synthetic and real CGH data, Stationary Wavelet Packet Transform (SWPT) is the best wavelet transform to analyze CGH signal in whole frequency. We also introduce a new bivariate shrinkage model which shows the relationship of CGH noisy coefficients of two scales in SWPT. Before smoothing, the symmetric extension is considered as a preprocessing step to save information at the border. Conclusions: We have designed the SWTP and the SWPT-Bi which are using the stationary wavelet packet transform with the hard thresholding and the new bivariate shrinkage estimator respectively to smooth the array CGH data. We demonstrate the effectiveness of our approach through theoretical and experimental exploration of a set of array CGH data, including both synthetic data and real data. The comparison results show that our method outperforms the previous approaches

Wavelet Based Color Image Denoising through a Bivariate Pearson Distribution

In this paper we proposed an efficient algorithm for Colo r Image Denoising through a Bivariate Pearson Distribution using Wavelet Which is based on Bayesian denoising and if Bayesian denoising is used for recovering image from the noisy image the performance is strictly depend on the correctness of the distribution that is used to describe the data. In the denoising process we require a selection of p roper model for distribution. To describe the image data bivariate pearson distribution is used and Gaussian distribution is used to describe the noise particles in this paper. For gray scale image lots of extensive works has been don e in this field but fo r colour image denoising using bivariate pearson distribution based on bayesian denoising gives us tremendous result for analy sing coloured images which can be used in several advanced applications. The bivariate probability density function (pdf) takes in t o account the Gaussian dependency among wavelet coefficients. The experimental results show that the proposed technique outperforms sev eral exiting methods both visually and in terms of peak signal - to - noise ratio (PSNR)

HYPERSPECTRAL IMAGE DENOISING USING MULTIPLE LINEAR REGRESSION AND BIVARIATE SHRINKAGE WITH 2-D DUAL-TREE COMPLEX WAVELET IN THE SPECTRAL DERIVATIVE DOMAIN

In this paper, a new denoising method is proposed for hyperspectral remote sensing images, and tested on both the simulated and the real-life datacubes. Predicted datacube of the hyperspectral images is calculated by multiple linear regression in the spectral domain based on the strong spectral correlation of the useful signal and the inter-band uncorrelation of the random noise terms in hyperspectral images. A two dimensional dual-tree complex wavelet transform is performed in the spectral derivative domain, where the noise level is elevated temporarily to avoid signal deformation during the wavelet denoising, and then the bivariate shrinkage is used to shrink the wavelet coefficients. Simulated experimental results demonstrate that the proposed method obtains better results than the other denoising methods proposed in the reference, improves the signal to noise ratio up to 0.5dB to 10dB. The real-life data experiment shows that the proposed method is valid and effective

Review on Colour Image Denoising using Wavelet Soft Thresholding Technique

In this modern age of communication the image and video is important as Visual information transmitted in the form of digital images, but after the transmission image is often ruined with noise. Therefore the received image needs to be processing before it can be used for further applications. Image denoising implicates the manipulation of the image data to produce a high quality of image without any noise. Most of the work which had done in color scale image is by filter domain approach, but we think that the transform domain approach give great result in the field of color image denoising.. This paper reviews the several types of noise which corrupted the color image and also the existing denoising algorithms based on wavelet threshodling technique. DOI: 10.17762/ijritcc2321-8169.15039