Data-driven tight frame learning scheme based on local and non-local sparsity with application to image recovery
DOI10.1007/s10915-016-0205-xzbMath1370.65012OpenAlexW2405544404MaRDI QIDQ2014022
Publication date: 10 August 2017
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-016-0205-x
image processingnumerical experimentimage restorationsparse representationwavelet framedata-drivenframe learning schemenon-local similarity
Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Numerical methods for wavelets (65T60) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Wavelet frame based image restoration via combined sparsity and nonlocal prior of coefficients
- A new TV-Stokes model with augmented Lagrangian method for image denoising and deconvolution
- A note on the dual treatment of higher-order regularization functionals
- Sparse directional image representations using the discrete shearlet transform
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- Framelets: MRA-based constructions of wavelet frames
- How to compare noisy patches? patch similarity beyond Gaussian noise
- An efficient algorithm for \(\ell_{0}\) minimization in wavelet frame based image restoration
- Data-driven tight frame construction and image denoising
- Data-driven multi-scale non-local wavelet frame construction and image recovery
- Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
- Learning Multiscale Sparse Representations for Image and Video Restoration
- New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities
- Generalized Tree-Based Wavelet Transform
- $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
- $\ell _0$ Minimization for wavelet frame based image restoration
- Clustering-Based Denoising With Locally Learned Dictionaries
- BM3D Frames and Variational Image Deblurring
- Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach
- Hessian Schatten-Norm Regularization for Linear Inverse Problems
- Image Processing Using Smooth Ordering of its Patches
- Deblurring and Denoising of Images by Nonlocal Functionals
- A Review of Image Denoising Algorithms, with a New One
- Bandelet Image Approximation and Compression
- Total Generalized Variation
This page was built for publication: Data-driven tight frame learning scheme based on local and non-local sparsity with application to image recovery
