We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier type deconvolution kernel density estimator based on the logarithm of the squared process is proposed to estimate the volatility density. Expansions of the bias and bounds on the variance are derived.

Approximate deconvolution forms a mathematical framework for the structural modeling of turbulence. The sub-filter scale flow quantities are typically recovered by using the Van Cittert iterative procedure. In this paper, however, we put forth a generalized approach for the iterative deconvolution process of sub-filter scale recovery of turbulent flows by introducing Krylov space iterative meth...

We present a Semi-Blind method for image deconvolution. This method uses a pre-processed image (via the shock filter) as an initial condition for total variation (TV) minimizing blind deconvolution. Using shock filter gives good information on location of the edges, and using variational functional such as Chan and Wong [T.F. Chan and C.K. Wong, Total variation blind deconvolution, IEEE Trans I...

term in (A.12) be negligible compared with (35), we must further require that (T =B 2)e 0 =B approaches zero. This rather unusual requirement simply assures that the modeling errors (bias squared) be negligible compared with the error variance. The derivation of the bound in (30) follows similarly by slightly modifying the definition of D D D: REFERENCES [1] S. Bellini and F. Rocca, " Asymptoti...

In this paper, Shalvi and Weinstein's 1-dimensional (1-D) computationally efficient super-exponential (SE) algorithm for blind deconvolution is extended to 2-dimensional (2-D) SE algorithm. Then a noiseinsensitive 2-D blind system identification (BSI) algorithm using the computationally efficient 2-D SE algorithm is proposed for the estimation of 2-D linear shift-invariant (LSI) systems. Moreov...

Microwave radiometer data is affected by many factors during the imaging process, including the antenna pattern, system noise, and the curvature of the Earth. Existing deconvolution methods such as Wiener filtering handle this degradation problem in the Fourier domain. However, under complex degradation conditions, the Wiener filtering results are not accurate. In this paper, a convolutional ne...

It is well known that blind channel deconvolution enables the receiver to equalize the channel simply by analyzing the received digital signal. Much of the work in 1990’s faces the challenge presented by multiple-output systems, exploiting cyclostationarity properties and multivariate formulation of the incoming data. Our proposal is twofold: on one hand, we develop a theoretical analysis of a ...

Hermes is a Single-Input Single-Output (SISO) underwater acoustic modem that achieves very high-bit rate digital communications in ports and shallow waters. Here, the authors study the capability of Hermes to support Multiple-Input-Multiple-Output (MIMO) technology. A least-square channel estimation algorithm is used to evaluate multiple MIMO channel impulse responses at the receiver end. A dec...

Deconvolution is an indispensable tool in image processing and computer vision. It commonly employs fast Fourier transform (FFT) to simplify computation. This operator, however, needs to transform from and to the frequency domain and loses spatial information when processing irregular regions. We propose an efficient spatial deconvolution method that can incorporate sparse priors to suppress no...