Sentence examples for uniform regularization from inspiring English sources

Several techniques have been developed to tackle this problem including (a) introduction of spatially variant regularization [ 7, 8] besides spatially uniform regularization, and (b) usage of hard spatial priors for functional DOT in human brain imaging to enforce the reconstructed images at the correct depths or locations [ 1].

Such crosstalk is partially due to the suboptimal regularization and update of the Jacobian matrix (which relates a small change in measured data to changes in optical parameters) as pointed out by Eames [ 31], who proposed a Jacobian normalization technique in non-linear DOT that allows more uniform regularization and parameter update in image reconstruction, hence reducing imaging crosstalk.

Thus, we obtain additional stronger regularization on the uniform and weaker regularization on the oscillatory patches, which significantly improves resulting image quality.

The spheres reconstructed using NM 530c are more uniform due to the noise regularization applied during the NM 530c reconstruction and the Butterworth post-filtering, but are similar in shape to those obtained using MCR.

We explain this effect by observing that, near these data, viscous regularization is non-uniform as the viscosity tends to zero.

The myocardium phantom profiles in Fig. 10 for MCR and NM 530c are similar, but the presence of noise regularization produced a more uniform myocardial distribution for NM 530c, as is evident in the individual slices.

By manually selecting regions of necrosis, viable tumor and other tissues (including, but not limited to: skin, adipose tissue, and normal margins) the software was trained with the following settings (1000 iterations of uniform distribution with 0.01 as a regularization parameter over 20 stage iterations with eight and three iterations per first and second stage, respectively).

In particular, we investigated the performance of four different regularizations: (i) ridge with uniform prior, (ii) ridge with mean prior, (iii) LASSO and (iv) elastic net.

In this paper a Fourier series method based on regularization is developed both for uniform and random design when a restriction on the complexity of the curve such as additivity is considered in order to circumvent the problem.

Different regularization strength of the reflectance is enforced to get more accurate results for non-uniform illumination images.

The results in this section concern uniform Gaussian estimates for the heat kernel of certain degenerate parabolic differential equations, and their parabolic regularizations.