Hyperspectral Image Denoising
19 papers with code • 3 benchmarks • 1 datasets
Most implemented papers
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
Furthermore, we present a method for training DNNs for denoising HSIs which are not spatially related to the training dataset, i. e., training on ground-level HSIs for denoising HSIs with other perspectives including airborne, drone-borne, and space-borne.
Deep Plug-and-Play Prior for Hyperspectral Image Restoration
Deep-learning-based hyperspectral image (HSI) restoration methods have gained great popularity for their remarkable performance but often demand expensive network retraining whenever the specifics of task changes.
Deep Sparse and Low-Rank Prior for Hyperspectral Image Denoising
The combination of a sparse and low-rank prior with a DIP views the CNN-based denoising method similar to a model-based method, inheriting the advantages of both model-based and CNN-based methods.
Mixed Attention Network for Hyperspectral Image Denoising
However, existing methods show limitations in exploring the spectral correlations across different bands and feature interactions within each band.
Hybrid Spectral Denoising Transformer with Guided Attention
Challenges in adapting transformer for HSI arise from the capabilities to tackle existing limitations of CNN-based methods in capturing the global and local spatial-spectral correlations while maintaining efficiency and flexibility.
Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising
In this paper, we address these issues by proposing a spectral enhanced rectangle Transformer, driving it to explore the non-local spatial similarity and global spectral low-rank property of HSIs.
Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising
Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module.
Hyperspectral Image Denoising via Self-Modulating Convolutional Neural Networks
At the core of the model lies a novel block, which we call spectral self-modulating residual block (SSMRB), that allows the network to transform the features in an adaptive manner based on the adjacent spectral data, enhancing the network's ability to handle complex noise.
Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising
To enhance the modeling of both global and local features, we have devised a convolution and attention fusion module aimed at capturing long-range dependencies and neighborhood spectral correlations.