Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity.
A general framework for solving image inverse problems with piecewise linear estimations is introduced in this paper. The approach is based on Gaussian mixture models, which are estimated via a maximum a posteriori expectation-maximization algorithm. A dual mathematical interpretation of the proposed framework with a structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared with traditional sparse inverse problem techniques. We demonstrate that, in a number of image inverse problems, including interpolation, zooming, and deblurring of narrow kernels, the same simple and computationally efficient algorithm yields results in the same ballpark as that of the state of the art.
Duke Scholars
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Related Subject Headings
- Sensitivity and Specificity
- Reproducibility of Results
- Normal Distribution
- Models, Statistical
- Linear Models
- Image Interpretation, Computer-Assisted
- Image Enhancement
- Computer Simulation
- Artificial Intelligence & Image Processing
- Artifacts
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Sensitivity and Specificity
- Reproducibility of Results
- Normal Distribution
- Models, Statistical
- Linear Models
- Image Interpretation, Computer-Assisted
- Image Enhancement
- Computer Simulation
- Artificial Intelligence & Image Processing
- Artifacts