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Computer vision requires the solution of many ill-posed problems
such as optical flow, structure from motion, shape from shading,
surface reconstruction, image restoration and edge detection.
Regularization is a popular method to solve ill-posed problems, in
which the solution is sought by minimization of a sum of two
weighted terms, one measuring the error arising from the ill-posed
model, the other indicating the distance between the solution and
some class of solutions chosen on the basis of prior knowledge
(smoothness, or other prior information). One of important issues
in regularization is choosing optimal weight(or regularization
parameter). Existing methods for choosing regularization
parameters either require the prior information on noise in the
data, or are heuristic graphical methods. In this work we apply a
new method for choosing near-optimal regularization parameters by
approximately minimizing the distance between the true solution
and the family of regularized solutions.
- C. Yang, R. Duraiswami and L. Davis. Near-Optimal Regularization Parameters for Applications in Computer Vision. In International Conference on Pattern Recognition, 569 - 573, vol.2, 2002.