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And is the regularization parameter which controls the tradeoff between the fidelity term and regularization term.
where λ balances between the fidelity term and the regularization term.
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In this work, the relationship and the fidelity term between the illumination and reflectance are not considered.
The first term of (1) is called the regularization term, the second term is called the fidelity term, and β>0 is the regularization parameter.
The fidelity term of the method can be obtained by Maximum a Posteriori (MAP).
where parameter γ balances the fidelity term and the sparsity of the solution and F is a feature extraction operator.
So far, variation methods achieve noise removing and contrast preserving mainly by adapting the fidelity term in TV regularization.
So for this, he introduced a new variable p for ∇u and then separate the calculation of the non-differentiability term and the fidelity term.
We remark that there are two regularization parameters and in the proposed algorithm, which controls the tradeoff between the image fidelity term and the regularization term.
where λ is a regularization parameter that balances the weight between the data fidelity term and the ℓ 1 regularization term.
where J X) is a regularization term specifying the prior knowledge of the HR image and λ is a scalar balancing between the quadratic fidelity term and the regularization term, such as the total variation (TV) regularization [6], edge smoothness [7], and gradient profile priors [8].
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