Image Reconstruction with Imperfect Forward Models and Applications in Deblurring
DOI10.1137/17M1141965zbMath1401.94018arXiv1708.01244OpenAlexW3104772903MaRDI QIDQ4686914
Publication date: 10 October 2018
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1708.01244
inverse problemsdeconvolutionblind deconvolutionuncertainty quantificationdeblurringresidual methodblind deblurringimperfect forward models
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Inverse problems in optimal control (49N45) Problems with incomplete information (optimization) (49N30)
Related Items (5)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Infimal convolution regularisation functionals of BV and \(\mathrm{L}^p\) spaces. I: The finite \(p\) case
- The residual method for regularizing ill-posed problems
- Properties of \(L^{1}-TGV^{2}\) : The one-dimensional case
- Graph Implementations for Nonsmooth Convex Programs
- Theory and Applications of Robust Optimization
- Making use of a partial order in solving inverse problems: II.
- Image deblurring with Poisson data: from cells to galaxies
- Decomposition of images by the anisotropic Rudin-Osher-Fatemi model
- Aspects of Total Variation RegularizedL1Function Approximation
- Convex Analysis
- Making use of a partial order in solving inverse problems
- Total Generalized Variation
This page was built for publication: Image Reconstruction with Imperfect Forward Models and Applications in Deblurring