Results 1 to 10 of about 415,263 (295)
Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution [PDF]
Sensors, 2023 Various super-resolution (SR) kernels in the degradation model deteriorate the performance of the SR algorithms, showing unpleasant artifacts in the output images. Hence, SR kernel estimation has been studied to improve the SR performance in several ways
Jongeun Park, Hansol Kim, Moon Gi Kang
doaj +2 more sources
Contingent kernel density estimation. [PDF]
PLoS ONE, 2012 Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points.
Scott Fortmann-Roe +2 more
doaj +6 more sources
Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
Applied Sciences, 2022 Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images.
Shanshan Liu +2 more
doaj +1 more source
Statistics & Risk Modeling, 2012 Abstract Many statistical estimation techniques for high-dimensional or functional data are based on a preliminary dimension reduction step, which consists in projecting the sample X 1,...,X n onto the first D eigenvectors of the Principal Component Analysis ...
Biau, Gérard, Mas, André
openaire +3 more sources
Multivariate kernel density estimation with a parametric support [PDF]
Opuscula Mathematica, 2009 We consider kernel density estimation in the multivariate case, focusing on the use of some elements of parametric estimation. We present a two-step method, based on a modification of the EM algorithm and the generalized kernel density estimator, and ...
Jolanta Jarnicka
doaj +1 more source
Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation. [PDF]
PLoS ONE, 2013 Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk.
Rongda Chen, Ze Wang
doaj +1 more source
Pixel-Level Kernel Estimation for Blind Super-Resolution
IEEE Access, 2021 Throughout the past several years, deep learning-based models have achieved success in super-resolution (SR). The majority of these works assume that low-resolution (LR) images are ‘uniformly’ degraded from their corresponding high ...
Jaihyun Lew, Euiyeon Kim, Jae-Pil Heo
doaj +1 more source
Robust kernel density estimation [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density estimator (KDE) with ideas from classical $M$-estimation.
Kim, JooSeuk, Scott, Clayton D.
openaire +2 more sources
Kernel density classification and boosting: an L2 sub analysis [PDF]
, 2005 Kernel density estimation is a commonly used approach to classification. However, most of the theoretical results for kernel methods apply to estimation per se and not necessarily to classification.
B.W. Silverman +25 more
core +2 more sources
Predictive accuracy of option pricing models considering high-frequency data
Ekonomski Vjesnik, 2021 Purpose: Recently, considerable attention has been given to forecasting, not only the mean and the variance, but also the entire probability density function (pdf) of the underlying asset.
Josip Arnerić, Maria Čuljak
doaj +1 more source