Hyperspectral Image Denoising
21 papers with code • 3 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Hyperspectral Image Denoising
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ICVL-HSI
Latest papers with no code
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping
On the one hand, the stripe noise is separately modeled by the tensor decomposition, which can effectively encode the spatial-spectral correlation of the stripe noise.
TDiffDe: A Truncated Diffusion Model for Remote Sensing Hyperspectral Image Denoising
Hyperspectral images play a crucial role in precision agriculture, environmental monitoring or ecological analysis.
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising
However, model-based approaches rely on hand-crafted priors and hyperparameters, while learning-based methods are incapable of estimating the inherent degradation patterns and noise distributions in the imaging procedure, which could inform supervised learning.
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
In the t-SVD, there are two key building blocks: (i) the low-rank enhanced transform and (ii) the accompanying low-rank characterization of transformed frontal slices.
A Hyper-weight Network for Hyperspectral Image Denoising
Extensive experiments verify that the proposed HWnet can help improve the generalization ability of a weighted model to adapt to more complex noise, and can also strengthen the weighted model by transferring the knowledge from another weighted model.
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
Hyperspectral image is unique and useful for its abundant spectral bands, but it subsequently requires extra elaborated treatments of the spatial-spectral correlation as well as the global correlation along the spectrum for building a robust and powerful HSI restoration algorithm.
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising
The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSI) for various applications such as mixed noise removal.
Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising
This paper tackles the challenging problem of hyperspectral (HS) image denoising.
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors
DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks.
Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising
In this study, the problem of computing a sparse representation of multi-dimensional visual data is considered.
