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For example, for a very informative discussion of the use of total variation regularization in the field of image processing, see the introduction of [7].

Interpretation follows the minimization of a functional with two terms: a term of conformity of the 3D interpretation to the image sequence first-order spatio-temporal variations, and a term of regularization based on anisotropic diffusion to preserve the boundaries of interpretation.

Open image in new window Fig. 1 The variation of residual norm with regularization parameter.

To avoid this power penalty, we propose to use a variation of ZF, channel inversion regularization (CIR), as a precoding scheme in MIMO-BC channels.

In addition, we applied the Wiener filter to the method in [8], instead of the bilateral-total variation (BTV) regularization to see the effect of the measurement validation only.

The method uses two kinds of regularization terms: the total variation transformation and the sparse transformation.

Open image in new window Fig. 2 The variation of GCV function value with regularization parameter.

For v fixed, the u subproblem (5) is the minimization of nonlocal total variation (NLTV) regularization energy in essence.

Total generalized variation (TGV) regularization model is one of the most effective methods for denoising and eliminating staircase effect.

The new form of regularization, termed the Modified Total Generalized Variation (MTGV) regularization, offers a compromise between distinguishing discrete and smooth features in the reconstructed distributions.

So the vectorial approach has already been used in most of the literature for RGB images, such as the work of [31 33] solved multichannel total variation (MTV) regularization reconstruction problem.