Results 141 to 150 of about 1,088,206 (196)

Real-time wavelet threshold denoising for laser speckle blood flow imaging. [PDF]

Sci Rep
Zhang L, Yang C, Liu D, Gao X, Li Z, Wang X, Zuo Z, He Z.   +7 more
europepmc   +1 more source

TCMNet: dynamic tanh enhanced 3D transformer with cascaded multiplicative fusion for pulmonary nodule detection. [PDF]

Sci Rep
Yao Z, Nian Y, Lian J, Ding F, Niu L.
europepmc   +1 more source

Stochastic and Statistical Analysis of Cnoidal, Snoidal, Dnoidal, Hyperbolic, Trigonometric and Exponential Wave Solutions of a Coupled Volatility Option-Pricing System. [PDF]

Entropy (Basel)
Abdalgadir LM, Ahmad S, Youniso B, Aldwoah K.   +3 more
europepmc   +1 more source

Nondestructive Detection of Eggshell Thickness Using Near-Infrared Spectroscopy Based on GBDT Feature Selection and an Improved CatBoost Algorithm. [PDF]

Foods
Li Z, Ji Y, Zhao C, Wang D, Zhou R.
europepmc   +1 more source

Joint Beamforming Design for Active RIS-Assisted ISAC Systems with Transmitter Hardware Impairments. [PDF]

Sensors (Basel)
Li Z, Hu J, Xing J.
europepmc   +1 more source

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A Variational Approach to Removing Multiplicative Noise

SIAM Journal on Applied Mathematics, 2008
This paper focuses on the problem of multiplicative noise removal. We draw our inspiration from the modeling of speckle noise. By using a MAP estimator, we can derive a functional whose minimizer corresponds to the denoised image we want to recover. Although the functional is not convex, we prove the existence of a minimizer and we show the capability ...
Jean-François Aujol, Gilles Aubert
exaly   +2 more sources

Nonlocal Filters for Removing Multiplicative Noise

, 2012
In this paper, we propose nonlocal filters for removing multiplicative noise in images. The considered filters are deduced in a weighted maximum likelihood estimation framework and the occurring weights are defined by a new similarity measure for comparing data corrupted by multiplicative noise.
Tanja Teuber, Annika Lang
openaire   +3 more sources

Noise-Loop Multiple Access

IEEE Transactions on Vehicular Technology, 2016
A fundamental problem for signal processing in wireless communications is the separation of multiple signals/users from their mixture. The problem is simpler if they are nonoverlapping in either the time domain or the frequency domain. Two signals may be overlapping in both time and frequency, but they can still be easily separated if they are ...
MUCCHI, LORENZO, RONGA, LUCA SIMONE, CHISCI, GIOVANNI   +2 more
openaire   +3 more sources