Results 31 to 40 of about 5,741 (199)
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection [PDF]
SIAM Journal on Imaging Sciences, 2014 Algorithms to solve variational regularization of ill-posed inverse problems usually involve operators that depend on a collection of continuous parameters. When these operators enjoy some (local) regularity, these parameters can be selected using the so-called Stein Unbiased Risk Estimate (SURE). While this selection is usually performed by exhaustive
Deledalle, Charles-Alban +3 more
openaire +3 more sources
Path Tracing Denoising Based on SURE Adaptive Sampling and Neural Network
IEEE Access, 2020 A novel reconstruction algorithm is presented to address the noise artifacts of path tracing. SURE (Stein's unbiased risk estimator) is adopted to estimate the noise level per pixel that guides adaptive sampling process.
Qiwei Xing, Chunyi Chen
doaj +1 more source
Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP
, 2021 Deep learning algorithms that rely on extensive training data are revolutionizing image recovery from ill-posed measurements. Training data is scarce in many imaging applications, including ultra-high-resolution imaging. The deep image prior (DIP) algorithm was introduced for single-shot image recovery, completely eliminating the need for training data.
John, Maneesh +3 more
openaire +2 more sources
An Extension of the Interscale SURE-LET Approach for Image Denoising
International Journal of Advanced Robotic Systems, 2014 In this paper, an extension of the interscale SURE-LET approach exploiting the interscale and intrascale dependencies of wavelet coefficients is proposed to improve denoising performance.
Lihong Cui +4 more
doaj +1 more source
Model Adaptation for Image Reconstruction using Generalized Stein's Unbiased Risk Estimator
, 2021 Deep learning image reconstruction algorithms often suffer from model mismatches when the acquisition scheme differs significantly from the forward model used during training. We introduce a Generalized Stein's Unbiased Risk Estimate (GSURE) loss metric to adapt the network to the measured k-space data and minimize model misfit impact.
Aggarwal, Hemant Kumar, Jacob, Mathews
openaire +2 more sources
Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended Stein’s unbiased risk estimator [PDF]
Health Information Science and Systems, 2021 Accurate segmentation of the breast tissue is a significant challenge in the analysis of breast MR images, especially analysis of breast images with low contrast. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results.
Yin, Xiao-Xia +6 more
openaire +3 more sources
Unsupervised Learning with Stein's Unbiased Risk Estimator
, 2018 Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this work, we revisit a classical idea: Stein's Unbiased Risk Estimator (SURE).
Metzler, Christopher A. +3 more
openaire +2 more sources
The Philosophical Significance of Stein's Paradox [PDF]
, 2017 Charles Stein discovered a paradox in 1955 that many statisticians think is of fundamental importance. Here we explore its philosophical implications.
Fitelson, Branden +2 more
core +1 more source
Adaptive Higher-order Spectral Estimators
, 2017 Many applications involve estimation of a signal matrix from a noisy data matrix. In such cases, it has been observed that estimators that shrink or truncate the singular values of the data matrix perform well when the signal matrix has approximately low
Gerard, David, Hoff, Peter
core +1 more source