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The fourth section is dedicated to evaluating the effect of the regularization on the field and flow models.
Similarly, the effect of the regularization parameter delta of the FxRLS algorithm is shown in Fig. 10 for α = 1.65.
The effect of the regularization is weak for, but starts to be significant for and is large for.
In order to better understand the effect of the regularization on the resulting model, we conduct a temporal resolution analysis similar to Olsen et al. (2009).
We show the effect of the regularization parameter in Figure 6. Figure 6 shows the experiments on the L-band 4-look AIRSAR image in San Francisco Bay (Figure 4) with and.
In that sense, the effect of the regularization parameter β is more important in the estimation of W I than W F, as imposing smoothness reduces the number of required samples.
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We study the effect of this regularization on the order of accuracy for a one-dimensional time-dependent problem.
The manufactured solution allows us to observe the expected convergence rates and explore the effects of the regularization in the optimization.
In this paper, we apply the l p (0 < p ≤ 1) sparsity regularization by partial use of the focal underdetermined system solver (FOCUSS) algorithm, which was introduced by He et al. [ 23] to solve linear underdetermined inverse problems, and investigate the effects of the regularization on the non-linear time-domain DOT image reconstruction.
Figure 16 Effect of the MRF regularization on the man-made patch classification.
In a second experiment, we investigate the effect of the TV regularization to an important SD-OCT system property, namely, sensitivity falloff.
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