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

Attached Files

#	File Type	File Size	Download
1	.pdf	8.93 MB	2111.09708
2	.zip	280.23 KB	T3SC-main

Multigraph-Based Low-Rank Tensor Approximation for Hyperspectral Image Restoration
Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification
Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection
Multimodal Hyperspectral Unmixing: Insights From Attention Networks
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction

Didn't find your answer? Contact Us

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

Attached Files

#

File Type

File Size

Download

Related Articles

l₀-l₁ Hybrid Total Variation Regularization and Its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing

Contact

Like Us On Facebook

Follow Us On Twitter

Links