Learning-based low-rank denoising
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Summary
This paper proposes a novel method that trains a learning network to predict the optimal thresholds of the singular value decomposition involved in the low-rank denoising of 2D images through the Singular Value Decomposition.
- Type
- article
- Published
- 2022-06-10
- Cited by
- 11
- References
- 34
- Access
- Open access
- OpenAlex
- https://openalex.org/W4281733151
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:249592132
Keywords
Noise reduction, Singular value decomposition, Pattern recognition (psychology), Video denoising, Artificial intelligence
References
- Nonlocal image denoising via adaptive tensor nuclear norm minimization
- ShearLab 3D
- Nonlocally Centralized Sparse Representation for Image Restoration
- Image denoising with block-matching and 3D filtering
- Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
- The dominance of Poisson noise in color digital cameras
- Weighted Nuclear Norm Minimization with Application to Image Denoising
- Noise Estimation From Digital Step-Model Signal
- A non-local algorithm for image denoising
- A Singular Value Thresholding Algorithm for Matrix Completion
- Some methods for classification and analysis of multivariate observations
- Speckle in ultrasound B-mode scans
- An Approach-Effect of an Exponential Distribution on different medical images
- A method for estimation and filtering of Gaussian noise in images
- 3D magnetic resonance image denoising using low-rank tensor approximation
- Adaptive regularizer learning for low rank approximation with application to image denoising
- Image Denoising using Various Wavelet Transforms: A Survey
- Video denoising using low rank tensor decomposition
- Ultrasound image despeckling using low rank matrix approximation approach
- Reweighted Low-Rank Matrix Analysis With Structural Smoothness for Image Denoising
Cited by
- Learning-based Framework for US Signals Super-resolution
- Super-resolution of 2D ultrasound images and videos
- HPC-based Solvers of Minimisation Problems for Signal Processing
- Influence of sorting measures on similar segment grouping based denoising algorithms
- An accurate paradigm for denoising degraded ultrasound images based on artificial intelligence systems
- Analysis and comparison of high-performance computing solvers for minimisation problems in signal processing
- Quantitative Phase Imaging Denoising Based on Denoising Diffusion Probabilistic Models
- Optimal Density Functions for Weighted Convolution in Learning Models
- Optimal Condition Evaluation for WNN Based Speckle Filtering Algorithms in SAR Image Denoising
- Influence of Sorting Measures on Similar Segment Grouping based Denoising Algorithms
- Beyond Convolution: A Taxonomy of Structured Operators for Learning-Based Image Processing
- In�uence of Sorting Measures on Similar Segment Grouping based Denoising Algorithms
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