In this paper we study a generalization of the Donoho-Johnstone denoising model for the case of the translation invariant wavelet transform. Instead of soft-thresholding coeecients of the classical orthogonal discrete wavelet transform, we study soft-thresholding of the co-eecients of the translation invariant discrete wavelet transform. This latter transform is not an orthogonal transformation...

In this paper, computer-aided detection of microcalcifications by multiresolution is performed. We firstly present methods of enhancement of mammograms. A combination of these methods is proposed and makes more obvious the features of the image. The second part of this paper deals with the multiscale image processing for detection of microcalcifications by wavelet and wavelet packets. For the w...

We study the application of Rissanen's Principle of Minimum Description Length (MDL) to the problem of wavelet denoising and compression for natural images. After making a connection between thresholding and model selection, we derive an MDL criterion based on a Laplacian model for noiseless wavelet coe cients. We nd that this approach leads to an adaptive thresholding rule. While achieving mea...

Biomedical images are generally corrupted by speckle noise and Gaussian noise. Speckle noise is multiplicative type whereas other noises like Gaussian noise are additive type. It is difficult to remove multiplicative noise from images. We have presented various techniques for removing speckle noise from images and image enhancement by thresholding using various spatial domain filters and Wavele...

Transmitting the information in the form of images has drawn much importance in the modern age. The images are often corrupted by various types of noises during acquisition and transmission. Such images have to be cleaned before using in any applications. Image denoising is a thirst area in image processing for decades. Wavelet transform has been an efficient tool for image representation for d...

Soft-thresholding is a sparse modeling method typically applied to wavelet denoising in statistical signal processing. It is also important in machine learning since it is an essential nature of the well-known LASSO (Least Absolute Shrinkage and Selection Operator). It is known that soft-thresholding, thus, LASSO suffers from a problem of dilemma between sparsity and generalization. This is cau...

Speech is being a fundamental way of communication among human beings. In many unavoidable situations, unwanted background noises are added to the speech signal. The proposed speech enhancement technique is to remove the background noise and to improve the quality of the speech signal. Noisy signal are decomposed by wavelet decomposition technique. Super soft thresholding technique is applied t...

This paper introduces a technique of bivariate thresholding based dual tree complex Wavelet transform(DTCWT) to remove additive and multiplicative both the noise signals. Since both the noises are different in nature hence it is difficult to remove both the noises by using single filter. Therefore two different filters are required denoise medical images which are corrupted by either of the noi...

We propose a wavelet based regression function estimator for the estimation of the regression function for a sequence of ?-missing random variables with a common one-dimensional probability density function. Some asymptotic properties of the proposed estimator based on block thresholding are investigated. It is found that the estimators achieve optimal minimax convergence rates over large class...

Interferometric synthetic aperture radar (InSAR) has been used widely in investigation of surface deformation, such as earthquake, volcano, ground subsistence and landslides. But there are still some obstacles standing in the stage of the application. One of those is the phase noise in the interferogra. In particular, there are much noise in the inteferograms derived in steep topographic area w...