We proposed a Hyper-Laplacian regularized low-rank tensor decomposition method combing with dimensionality reduction framework. Firstly, k-means++ algorithm is implemented to spectral bands and centers of each group are selected to reduce the HSI dimensionality in spectral direction. To jointly utilize ...
Sharma. A regularized model-based op- timization framework for pan-sharpening. IEEE Trans. On Image Processing, 23(6):2596–2608, 2014. 1, 3, 6 [4] C. Ballester, V. Caselles, L. Igual, J. Verdera, and B. Rouge´. A variational model for p+ xs image fusion. Intenational J....
Blind image deblurring via L1-regularized second-order gradient prior Article 28 April 2022 Two-stage image deblurring with L0 gradient minimization and non-local refinement Article 01 October 2015 Pyramidical Based Image Deblurring via Kernel Continuity Prior Article 04 March 2023 References Asi...
We created the Cauchy Hyper-graph Laplacian Non-negative Matrix Factorization technique (CHLNMF) for single-cell data clustering to overcome the issues raised above. To lessen the effect of noise, CHLNMF specifically substitutes the Euclidean distance in the conventional NMF with the Cauchy loss func...
The key advantage of the proposed method, termed as hyper-Laplacian regularized nonlocal low-rank matrix recovery (HyNLRMR), is to adopt insightful property, namely the nonlocal self-similarity across the spatial domain and the consistency along the spectral domain. Then, the alternative direction ...
For the flaws of the algorithm, this paper proposes a hyper-Laplacian regularized low-rank collaborative representation classification (HLCRC). The hyper-Laplacian regularizer is introduced into the low-rank collaborative representation model to maintain the multivariate geometric manifold structures between ...
The key advantage of the proposed method, termed as hyper-Laplacian regularized nonlocal low-rank matrix recovery (HyNLRMR), is to adopt insightful property, namely the nonlocal self-similarity across the spatial domain and the consistency along the spectral domain. Then, the alternative direction ...
In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed for learning genotype-phenotype associations. Specifically, a deep neural network is used first to explore the nonlinear relationships among samples....
The traditional regularized processing methods have a strong ability to improve the definition, but most of them may sacrifice texture details or introduce artifacts, because their fixed prior parameters cannot fully adapt to various kinds of scenes. To address this problem, we propose a novel fine...