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Efficient half-quadratic regularization with granularity control
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DOI：10.1016/S0262-8856(03)00005-2.png)
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Abstract
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In the last decade, several authors have proposed edge preserving regularization methods for solving ill posed problems in early vision. These techniques are based on potentials derived from robust M-Estimators. They are capable of detecting outliers in the data. finding the significant borders in noisy images and performing edge-preserving restorations. These methods, however. have some problems: they are computationally expensive. and often produce solutions which are either too smooth or too granular (with borders around small regions), In this paper, we present a new class of potentials that permits to separate robustness and granularity control, producing better results than the classical ones in both scalar- and vector-valued images. We also present a new fast, memory-limited minimization algorithm. (C) 2003 Elsevier Science B.V. All rights reserved.
Keywords:
half-quadratic regularization
edge-preserving regularization
image restoration
non-linear filtering
conjugate gradient algorithms
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