Hyperspectral Image Super-Resolution
18 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Hyperspectral Image Super-Resolution
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Multispectral Image Database
Most implemented papers
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs).
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
kli8996/HSISR
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With these contributions, our method is able to learn from heterogeneous datasets and lift the requirement for having a large amount of HD HSI training samples.
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-Net
yingutk/u2MDN
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With this design, the network allows to extract correlated spectral and spatial information from unregistered images that better preserves the spectral information.
Hyperspectral Image Super-Resolution With Optimized RGB Guidance
To overcome the limitations of existing hyperspectral cameras on spatial/temporal resolution, fusing a low resolution hyperspectral image (HSI) with a high resolution RGB (or multispectral) image into a high resolution HSI has been prevalent.
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning
Specifically, PZRes-Net learns a high resolution and \textit{zero-centric} residual image, which contains high-frequency spatial details of the scene across all spectral bands, from both inputs in a progressive fashion along the spectral dimension.
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
danfenghong/ECCV2020_CUCaNet
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ECCV 2020
The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR).
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and {the} learned prior to reconstruct the final HR HSI.
Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes
The required pre-processing steps have been defined and 13 pansharpening methods have been applied and evaluated for their ability to spectrally discriminate plastics from water.
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization
Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image.
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention.
