ENSURE: ENSEMBLE STEIN'S UNBIASED RISK ESTIMATOR FOR UNSUPERVISED LEARNING. [PDF]
Proc IEEE Int Conf Acoust Speech Signal Process, 2021 Deep learning algorithms are emerging as powerful alternatives to compressed sensing methods, offering improved image quality and computational efficiency.
Aggarwal HK, Pramanik A, Jacob M.
europepmc +4 more sources
Denoising PET images using singular value thresholding and Stein's unbiased risk estimate. [PDF]
Med Image Comput Comput Assist Interv, 2013 Image denoising is an important pre-processing step for accurately quantifying functional morphology and measuring activities of the tissues using PET images. Unlike structural imaging modalities, PET images have two difficulties: (1) the Gaussian noise model does not necessarily fit into PET imaging because the exact nature of noise propagation in PET
Bagci U, Mollura DJ.
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Optimization of compound regularization parameters based on Stein's unbiased risk estimate [PDF]
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on BDF algorithm, we develop a data-driven optimization scheme based on minimization of Stein's unbiased risk estimate (SURE ...
Feng Xue +4 more
semanticscholar +4 more sources
Estimating the Rank of a Nonnegative Matrix Factorization Model for Automatic Music Transcription Based on Stein’s Unbiased Risk Estimator [PDF]
Applied Sciences, 2020 In this paper, methods to estimate the number of basis vectors of the nonnegative matrix factorization (NMF) of automatic music transcription (AMT) systems are proposed.
Seokjin Lee
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Sensor-Oriented Framework for Underwater Acoustic Signal Classification Using EMD–Wavelet Filtering and Bayesian-Optimized Random Forest [PDF]
SensorsShip acoustic signal classification is essential for vessel identification, underwater navigation, and maritime security. Traditional methods struggle with the non-stationary nature and noise of ship acoustic signals, reducing classification accuracy. To
Sergii Babichev +4 more
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Tractable Evaluation of Stein's Unbiased Risk Estimate with Convex Regularizers [PDF]
IEEE Transactions on Signal Processing, 2022 Stein's unbiased risk estimate (SURE) gives an unbiased estimate of the $\boldsymbol{\ell}_{2}$ risk of any estimator of the mean of a Gaussian random vector.
Parth Nobel +2 more
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Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model [PDF]
Sensors, 2023 In GNSS-denied environments, especially when losing measurement sensor data, inertial navigation system (INS) accuracy is critical to the precise positioning of vehicles, and an accurate INS error compensation model is the most effective way to improve ...
Zhonghan Li +4 more
doaj +2 more sources
Automated Parameter Selection for Accelerated MRI Reconstruction via Low-Rank Modeling of Local k-Space Neighborhoods [PDF]
Zeitschrift für Medizinische Physik, 2023 Purpose: Image quality in accelerated MRI rests on careful selection of various reconstruction parameters. A common yet tedious and error-prone practice is to hand-tune each parameter to attain visually appealing reconstructions.
Efe Ilicak +2 more
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Self-supervised MRI denoising: leveraging Stein's unbiased risk estimator and spatially resolved noise maps. [PDF]
Sci Rep, 2023 AbstractThermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions. Recently, deep learning (DL)-based denoising methods achieved promising results by extracting complex feature representations from large data sets.
Pfaff L +8 more
europepmc +5 more sources
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 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, more importantly, when they will fail.
Ruangrawee Kitichotkul +3 more
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