Results 91 to 100 of about 1,088,206 (196)
An Adaptive Fractional-Order Variation Method for Multiplicative Noise Removal
This paper aims to develop a convex fractional-order variation model for image multiplicative noise removal, where the regularization parameter can be adjusted adaptively according to balancing principle at each iterations to control the trade-off ...
Du YK(杜英魁) +2 more
core
Multiplicative noise induced bistability and stochastic resonance
Stochastic resonance is a well established phenomenon, which proves relevant for a wide range of applications, of broad trans-disciplinary breath.
Giuliano Migliorini, Duccio Fanelli
doaj +1 more source
164 p.This dissertation is devoted to the frequency estimation in the presence of multiplicative noise and colored noise. One is intrigued by the observation that the received signal is corrupted by a multiplicative noise in most practical applications ...
Wang, Zhi
core +1 more source
A New Variational Approach for Multiplicative Noise and Blur Removal. [PDF]
Ullah A, Chen W, Khan MA, Sun H.
europepmc +1 more source
Multiplicative uncertainty, central bank transparency and optimal degree of conservativeness [PDF]
This paper extends the results of Kobayashi (2003) and Ciccarone and Marchetti (2009) by considering the optimal choice of central bank conservativeness. It is shown that the government can choose a sufficiently populist but opaque central banker so that
Meixing Dai
core
Multiplicative noise removal using primal-dual and reweighted alternating minimization. [PDF]
Wang X, Bi Y, Feng X, Huo L.
europepmc +1 more source
Wind turbines noise measurements inside homes
Wind energy is a primary source for achieving the objectives of the Oil Free Society. However, noise emission is often a significant problem encountered during wind turbines operation.
Ciaburro G. +3 more
core
Adaptive tight frame based multiplicative noise removal. [PDF]
Zhou W, Yang S, Zhang C, Fu S.
europepmc +1 more source
Fitting multiplicative models by robust alternating regressions. [PDF]
In this paper a robust approach for fitting multiplicative models is presented. Focus is on the factor analysis model, where we will estimate factor loadings and scores by a robust alternating regression algorithm. The approach is highly robust, and also
Croux, Christophe +3 more
core

