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Image denoising using orthonormal wavelet transform with stein unbiased risk estimator
2014 IEEE Students' Conference on Electrical, Electronics and Computer Science, 2014De-noising plays a vital role in the field of the image preprocessing. It is often a necessary to be taken, before the image data is analyzed. It attempts to remove whatever noise is present and retains the significant information, regardless of the frequency contents of the signal. It is entirely different content and retains low frequency content. De-
Manish Yadav, Swati Yadav, Dilip Sharma
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ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Recently, there have been several works on unsupervised learning for training deep learning based denoisers without clean images. Approaches based on Stein’s unbiased risk estimator (SURE) have shown promising results for training Gaussian deep denoisers.
Shakarim Soltanayev +3 more
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Recently, there have been several works on unsupervised learning for training deep learning based denoisers without clean images. Approaches based on Stein’s unbiased risk estimator (SURE) have shown promising results for training Gaussian deep denoisers.
Shakarim Soltanayev +3 more
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Pacific Rim Conference on Communications, Computers and Signal Processing
Most of the research works on music transcription assume a priori knowledge regarding the number of musical notes or instruments. This paper proposes two novel algorithms for Automatic Music Transcription (AMT).
Bauyrzhan Kurmangaliyev, M. Akhtar
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Most of the research works on music transcription assume a priori knowledge regarding the number of musical notes or instruments. This paper proposes two novel algorithms for Automatic Music Transcription (AMT).
Bauyrzhan Kurmangaliyev, M. Akhtar
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Stein's Approach Based MVDR Filter Modification
IEEE Signal Processing LettersWe consider a modification of the minimum variance distortionless response (MVDR) filter using Stein unbiased risk estimation (SURE). The starting point of this modification lies in the observation that the component of the MVDR filter in the subspace ...
Olivier Besson
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ADA-PT: An Adaptive Parameter Tuning Strategy Based on the Weighted Stein Unbiased Risk Estimator
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018The performance of iterative algorithms aimed at solving a regularized least squares problem typically depends on the value of some regularization parameter. Tuning the regularization parameter value is a fundamental step necessary to control the strength of the regularization and hence ensure a good performance.
Ammanouil, Rita +2 more
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Implicit Regularization for Improving Phase-based EPT with Stein’s Unbiased Risk Estimator
ISMRM Annual MeetingPhase-based EPT algorithm is extremely sensitive to noise. Although various denoising algorithms have been introduced to suppress noise amplification, residual artifact cause instability conductivity error or broadening boundary artifact. In this work, we propose a novel generative network trained with Stein’s unbiased risk estimator under the purely ...
Chuanjiang Cui +3 more
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Single Snapshot Direction of Arrival Estimation Using the EP-SURE-SBL Algorithm
IEEE International Conference on Acoustics, Speech, and Signal ProcessingGrid-based methods in sparse signal reconstruction (SSR) are well-regarded for their efficacy in direction-of-arrival (DoA) estimation. This paper presents the EP (Expectation Propagation)-SURE (Stein's Unbiased Risk Estimate)-SBL (Sparse Bayesian ...
Fangqing Xiao, Dirk T. M. Slock
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SURE Guided Posterior Sampling: Trajectory Correction for Diffusion-Based Inverse Problems
arXiv.orgDiffusion models have emerged as powerful learned priors for solving inverse problems. However, current iterative solving approaches which alternate between diffusion sampling and data consistency steps typically require hundreds or thousands of steps to
Minwoo Kim, Hongki Lim
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Iraqi journal of statistical sciences
This paper uses the Maximum Likelihood Estimation method to investigate the impact of data contamination on the accuracy of parameter estimation for the Gamma distribution. A de-noising approach based on wavelet shrinkage has been proposed to address the
H. Taha, T. Ali, H. Hayawi
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This paper uses the Maximum Likelihood Estimation method to investigate the impact of data contamination on the accuracy of parameter estimation for the Gamma distribution. A de-noising approach based on wavelet shrinkage has been proposed to address the
H. Taha, T. Ali, H. Hayawi
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A recursive unbiased risk estimate for the analysis-based ℓ 1 minimization
Signal, Image and Video Processing, 2021Jing Li
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