By coupling the wavelet transform with a particular nonlinear shrinking function, the Red-telescopic optimal wavelet estimation of the risk (TOWER) method is introduced for removing noise from signals. It is shown that the method yields convergence of the 2 risk to the actual solution with optimal rate. Moreover, the method is proved to be asymptotically efficient when the regularization parame...

Interpolating the missing traces of regularly or irregularly sampled seismic record is an exceedingly important issue in the geophysical community. Many modern acquisition and reconstruction methods are designed to exploit the transform domain sparsity of the few randomly recorded but informative seismic data using thresholding techniques. In this paper, to regularize randomly sampled seismic d...

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...

Current methods for thresholding functional magnetic resonance imaging (fMRI) maps are based on the well-known hypothesis-test framework, optimal for addressing novel theoretical claims. However, these methods as typically practiced have a strong bias toward protecting the null hypothesis, and thus may not provide an optimal balance between specificity and sensitivity in forming activation maps...

We use prior and boundary estimates as the approximation of outside probability and establish our beam thresholding strategies based on these estimates. Lexical items, e.g. head word and head tag, are also incorporated to lexicalized prior and boundary estimates. Experiments on the Penn Chinese Treebank show that beam thresholding with lexicalized prior works much better than that with unlexica...

Thresholdging video images is very challenging due to the fact that image background generally has low resolution and is also more complicated and highly distorted than document images. As a result, thresholding methods that work well for document images may not work effectively for video images in some applications. This paper investigates the issue of thresholding video images for text detect...

This paper presents a novel wavelet-based denoising method using coefficient thresholding technique. The proposed method uses the adaptive thresholding which overcome the shortcomings of discontinuous function in hard-thresholding and also can eliminate the permanent bias in soft-thresholding. The qualitative evaluation of the denoising performance has shown that the proposed method cancels noi...