Name
Abstract | ||
|---|---|---|
Hyperspectral images are corrupted by noise during their acquisition. In this work, we propose to efficiently denoise hyperspectral images under two assumptions: (i) noiseless hyperspectral images in matrix form are low-rank, and (ii) image patches are sparse in a proper representation domain defined through a dictionary. These two assumptions have already led to state-of-the-art denoising methods using fixed Wavelet transforms. We propose to rather learn the dictionary from hyperspectral images, a task commonly known as dictionary learning. We show that the dictionary learning approach is more efficient to denoise hyperspectral images than state-of-the-art methods with fixed dictionaries, at the cost of a larger computation time. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WHISPERS.2019.8921110 | 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | DocType | ISSN |
Hyperspectral image,denoising,sparsity,low-rank,dictionary learning | Conference | 2158-6268 |
ISBN | Citations | PageRank |
978-1-7281-5295-0 | 0 | 0.34 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cassio Fraga Dantas | 1 | 3 | 2.78 |
| Jeremy Cohen | 2 | 0 | 3.04 |
| Rémi Gribonval | 3 | 1207 | 83.59 |