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General choice of the regularization functional in regularized image restoration
1Dept. of Electr. and Comput. Eng., Minnesota Univ., Duluth, MN.
Summary
This study introduces a novel iteration-adaptive regularization functional for image restoration. This method extracts prior information from partially restored images, improving the trade-off between data fidelity and solution smoothness.
Area of Science:
- Image processing
- Computational mathematics
Background:
- Determining the regularization parameter is crucial for regularized image restoration.
- Existing methods for parameter determination have limitations.
Purpose of the Study:
- To propose a new paradigm for regularization parameter determination.
- To introduce an iteration-adaptive regularization functional for image restoration.
Main Methods:
- Utilizing prior information from partially restored images at each iteration.
- Proposing and investigating properties of a regularization functional.
- Developing an iterative algorithm for image restoration using the functional.
Main Results:
- The proposed iteration-adaptive regularization functionals lead to a smoothing functional with a global minimum.
- Iterative optimization is independent of initial conditions.
- Convergence of the algorithm is established.
Conclusions:
- The new approach enables simultaneous determination of the regularization functional's value and image restoration.
- The method offers improved control over the fidelity-smoothness trade-off in image restoration.
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