Results 151 to 160 of about 5,741 (199)

Interscale Stein's Unbiased Risk Estimate and Intrascale Feature Patches Distance Constraint for Image Denoising

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2010
Qieshi Zhang, Sei‐ichiro Kamata, Alireza Ahrary   +2 more
semanticscholar   +3 more sources

Stein's Unbiased Risk Estimate (SURE) and Distance Constraint Combined Image Denoising in Wavelet Domain

Signal Processing, Pattern Recognition and Applications, 2010
Qieshi Zhang, Sei‐ichiro Kamata
semanticscholar   +3 more sources

On Stein’s unbiased risk estimate for reduced rank estimators

Statistics & Probability Letters, 2018
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Hansen, Niels Richard
openaire   +4 more sources

Prospect of Stein's Unbiased Risk Estimate as Objective Function for Parameter Optimization in Image Denoising Algorithms – A Case Study on Gaussian Smoothing Kernel

International Conferences on Data Science and Engineering, 2019
Stein's Unbiased Risk Estimate (SURE) is considered as an indirect method for predicting Mean Squared Error (MSE) in the absence of ground-truth, as its computation requires only noisy observation and denoised image.
V.R. Simi, Damodar Reddy Edla, Justin Joseph, Venkatanareshbabu Kuppili   +3 more
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On estimation and prediction of geostatistical regression models via a corrected Stein's unbiased risk estimator

Environmetrics, 2017
We consider geostatistical regression models to predict spatial variables of interest, where likelihood‐based methods are used to estimate model parameters. It is known that parameters in the Matérn covariogram cannot be estimated well, even when increasing amounts of data are collected densely in a fixed domain.
H. Yang, Chun-Shu Chen
semanticscholar   +2 more sources

Deep Network Regularization for Phase-Based Magnetic Resonance Electrical Properties Tomography With Stein's Unbiased Risk Estimator

IEEE Transactions on Biomedical Engineering
Magnetic resonance imaging (MRI) can estimate tissue conductivity values using phase-based magnetic resonance electrical properties tomography (MR-EPT). However, this method is prone to noise amplification due to the Laplacian operator's sensitivity.
Chuanjiang Cui, K. Jung, M. A. Al-masni, Jun-Hyeong Kim, Soo-Yeon Kim, Mina Park, Shao Ying Huang, S. Chun, Dong-Hyun Kim   +8 more
semanticscholar   +3 more sources

Suremap: Predicting Uncertainty in Cnn-Based Image Reconstructions Using Stein’s Unbiased Risk Estimate

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and,
Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong, Gordon Wetzstein   +3 more
semanticscholar   +1 more source

Parameter selection for smoothing splines using Stein's Unbiased Risk Estimator

The 2011 International Joint Conference on Neural Networks, 2011
A challenging problem in smoothing spline regression is determining a value for the smoothing parameter. The parameter establishes the tradeoff between the closeness of the data, versus the smoothness of the regression function. This paper proposes a new method of finding the optimum smoothness value based on Stein's Unbiased Risk Estimator (SURE ...
Sepideh Seifzadeh, Mohammad Rostami, Ali Ghodsi, Fakhreddine Karray   +3 more
openaire   +1 more source

Adaptive singular value shrinkage estimate for low rank tensor denoising

Random Matrices. Theory and Applications, 2022
Recently, tensors are widely used to represent higher-order data with internal spatial or temporal relations, e.g. images, videos, hyperspectral images (HSIs).
Zerui Tao, Zhouping Li
semanticscholar   +1 more source

Automatic basis selection for RBF networks using Stein's unbiased risk estimator

Proceedings of the International Joint Conference on Neural Networks, 2003., 2004
The problem of selecting the appropriate number of basis functions is a critical issue for radial basis function neural networks. An RBF network with an overlay restricted basis gives poor predictions on new data, since the model has too little flexibility.
A. Ghodsi, D. Schuurmans
openaire   +1 more source