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home/Knowledge Base/CODES/Hyperspectral/A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration

December 17, 2021

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
Théo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot and Julien Mairal

NeurIPS 2021

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Categories
  • CODES
    • Anomaly
    • Change Detection
    • Classification of Hyperspectral Images
    • Classification of Remote Sensing Data
    • Data fusion: hyperspectral + Lidar
    • Data fusion: Hyperspectral + Multispectral
    • Deep Learning
    • Denoising
    • Feature Extraction
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    • Graphs, Manifold
    • Hyperspectral
    • Hyperspectral remote sensing
    • Hyperspectral Super Resolution
    • Infrared
    • Machine Learning in Remote Sensing
    • Multi-modal
    • Object Detection
    • Pansharpening
    • Registration
    • Sequences
    • Spectral Unmixing
    • Super Resolution
    • Synthetic Aperture Radar and Radar Sounder
    • Target Detection
    • Tensor
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  l₀-l₁ Hybrid Total Variation Regularization and Its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing

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