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In this work, regularization (with a GGMRF prior model) was used for the reconstruction of γ4.
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In this work, a regularization method has been successfully applied to the inverse ultraparabolic problem.
The significance of this generalization is that recent work on regularization methods requires the use of blobs designed to satisfy certain properties, such as zero moment conditions and fast decay, and thus it is absolutely necessary to have the system of images starting from an arbitrary blob.
In this work the regularization of the excess predictors is performed by a hierarchical Bayesian LASSO (Park and Casella 2008), by specifying a Laplace prior density for the regression coefficients.
Hereby, we add to the objective used in previous works a regularization term that controls the global shape of the energy landscape, that is the function actually maximized by the dynamics.
The proposed encoder is regularized by an extension of previous work on contractive regularization.
According to our previous work [8], the regularization parameter for RZA-NLMF is set as λ = 5 × 10-8 so that it can exploit signal sparsity robustly.
In this work a novel regularization technique based on the singular value decomposition (SVD) is presented that preserves the spectral prior information while regularizing the Jacobian matrix, leading to dramatically reduced crosstalk between the recovered parameters.
In such context, regularization works using sets of functions of (varepsilon )-approximation-type scale.
Many previous works manually design regularization to simultaneously estimate the latent sharp image and the blur kernel under maximum a posterior framework.
Image regularization works by attempting to determine the missing pixel values, denoted by an image mask, as seen in Fig. 6.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com