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It also uses L2 regularization, with a Gaussian prior variance set to 4.0.
Image regularization with PDE is again based on a measure of local parameter variations.
Chen et al. [3] applied regularization with Cesàro sum technique for the derivative of the double layer potential.
For many years, image regularization with discontinuities (edges) preservation has been studied in the computer vision community.
This paper uses the Tikhonov regularization with a non-negative constraint of solution for NMR T2 inversion.
In this paper we propose to unify the POCS regularization with the DEDR method originally developed in [7, 8].
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However, the solution to NMF is not unique so various regularizations with prior knowledge should be taken into account to reduce the number of solutions.
This is equivalent to combination of ℓ1-regularization with ℓ∞-regularization.
Some literatures (Yu et al., 2010) replace ℓ1-regularization with another regularization.
The l p sparsity regularizations with p = 1/2 and 1/4 provide highly localized targets.
Figure 6 shows the reconstructed μ a distributions using Tikhonov and the l p sparsity regularizations with p = 1, 1/2, and 1/4.
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