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Found 6 papers, 2 papers with code
Neural Gradient Regularizer
NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer.
Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
Recent research have made significant progress by adopting two insightful tensor priors, i. e., global low-rankness (L) and local smoothness (S) across different tensor modes, which are always encoded as a sum of two separate regularization terms into the recovery models.
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an HSI data $ \mathbf{X} \in \mathbb{R}^{MN\times B}$.
Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices
It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques.
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing
The 3-D total variation (3DTV) is a powerful regularization term, which encodes the local smoothness prior structure underlying a hyper-spectral image (HSI), for general HSI processing tasks.
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part respectively.
