An Object Oriented Approach to Image Restoration in Matlab


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Regularization by Filtering

One method to choose regularization parameters is:
Generalized Cross Validation
For example, in Tikhonov, choose  to minimize


Computational Issues:

1. Choose an appropriate basis for U and V
e.g., singular vectors, Fourier vectors, ...
2. Efficient algorithm (speed and storage) to compute


Iterative Regularization

Some examples:
1. Conjugate Gradient (CG)
2. (Constrained) Steepest Descent (EM-LS)
3. Lucy-Richardson (EM-ML)


To-Do List
• Stopping criteria for iterative methods.
• Better understanding of preconditioning EM-LS.
• Include other regularization by filtering methods.
• Include other iterative regularization methods.
(e.g., Blind Image Restoration – Plemmons, Jefferies)
• Test codes using more real applications.