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Found 6 papers, 0 papers with code
Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising
In this paper, we propose the multi-modal and frequency-weighted tensor nuclear norm (MFWTNN) and the non-convex MFWTNN for HSI denoising tasks.
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm
In the Fourier transform domain of HSIs, different frequency slices (FS) contain different information; different singular values (SVs) of each FS also represent different information.
Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation
Instead of traditional bandwise total variation, we use the SSTV regularization to simultaneously consider global spatial structure and spectral correlation of neighboring bands.
Enhanced nonconvex low-rank approximation of tensor multi-modes for tensor completion
Higher-order low-rank tensor arises in many data processing applications and has attracted great interests.
Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation
From one aspect, local LR of HSIs is formulated using a non-convex $L_{\gamma}$-norm, which provides a closer approximation to the matrix rank than the traditional NN.
Tensor completion using enhanced multiple modes low-rank prior and total variation
In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor.
