Nonlocal data fidelity termIn this paper, we propose an improved model for image decomposition and denoising based on Shearlet and nonlocal data fidelity term. The model splits an image into three parts: the cartoon component modeled by total variation (TV) space, the texture component modeled ...
The data distribution is [*,*,Block], where “Block” distribution assigns a contiguous block of array elements to each processor and “*” means no distribution in that dimension. In the computation of the finite volume scheme, each processor will be responsible for calculations in the sub...
The parameter, B, is defined as a block of spatial area whose location and geometry are known. The optimal predictor is obtained by minimizing the mean-squared prediction error defined as $${\sigma }_{e}^{2}\equiv E{(Z\left(B\right)-p\left(Z; B\right))}^{2}$$ (111) with ...
aArchitecture overview, for details on the nuclear and electronic (charge/spin) embeddings and basis functions, refer to Eqs. (9), (10), and (13), respectively.bInteraction module, see Eq. (11).cLocal interaction block, see Eq. (12).dNonlocal interaction block, see Eq. (18).eResidua...
Each band of HSI is a gray image, and it can be denoised band-wise by traditional gray-level image denoising methods, such as the nonlocal-based algorithm [19], K-singular value decomposition (K-SVD) [20], and block-matching 3-D filtering (BM3D) [21]. The band-wise methods ignore...
block-diagonal preconditioner that is based on approximate Schur complement (implemented in ex5p), and a newly implemented solver DivFreeSolver, which exploits a multilevel decomposition of the Raviart-Thomas space and its divergence-free subspace. See the miniapps/solvers directory for more det...
First, it employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique for robust classification of blocks in noisy images. Second, the local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching ...
The proposed method employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique to achieve robust block classification in noisy images. Then, a local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching...
can be proven to hold for any set of distinct numbers\lbrace m_i^2 \rbrace[36], it is not necessary to work with the expression of the coefficientsC_jin terms ofm_ito verify the cancellation of the singularity, since (40) is merely a consequence of the partial fraction decomposition, as...
For every block in each group 𝐶𝑖Ci, the data are transformed into the wavelet domain. Step 3. Calculation of nonlocal mean value of wavelet coefficients: Since nonlocal weights are obtained in Step 1, the nonlocal mean value of the wavelet coefficients in each group is calculated using...