"data fidelity" is a correct and usable phrase in written English.
It is often used in technical or scientific writing to refer to the accuracy or reliability of data. Example: The data fidelity of the experiment was tested multiple times to ensure accurate results. In this sentence, "data fidelity" is used to describe how well the data from the experiment matches the real-world results. It implies that the data is trustworthy and has a high level of accuracy.
It contains two terms, namely the vectorial data fidelity term and the non-local vectorial total variation term.
The regularization parameter, which controls the trade-off between data fidelity and solution size of the regularization problem, exerts a crucial effect on the solution.
Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term.
In order to suppress measurement errors and to identify sharp features, we propose a new regularization integrating an l1 data fidelity with a total variation1 penalty term to reconstruct a piecewise constant velocity field.
From numerical experiments, we can draw conclusions: (I) an l1 data fidelity can suppress measurement errors including Gaussian noise and non-Gaussian noise; (II) a total variation penalty term has the ability to identify sharp features.
The second approach overcoming the staircasing effect is to adopt the new data fidelity term.
This paper presents an augmented Lagrangian (AL) based method for designing of overcomplete dictionaries for sparse representation with general lq-data fidelity term (q ⩽ 2).
A fusion optimization model is then introduced, including the data-fidelity term capturing the statistics of mixed Poisson Gaussian noise, and a beta process analysis-based sparse representation regularization term.
Moreover, they use ℓ 1 or ℓ 2 norm for the data-fidelity term.
We first propose an energy functional based on the data-fidelity measure deduced from Poisson distribution.
We refer the reader to [17] for the derivation of this data-fidelity term using Bayesian statistics.
