Name
Title | ||
|---|---|---|
Locally-Adapted Convolution-Based Super-Resolution Of Irregularly-Sampled Ocean Remote Sensing Data |
Abstract | ||
|---|---|---|
Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal convolutional models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. We consider an application to the reconstruction of sea surface height (SSH) fields from two information sources, along-track altimeter data and sea surface temperature (SST) data. The reported experiments demonstrate the relevance of the proposed model, especially locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/icip.2017.8297095 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | Field | DocType |
Super-resolution, convolutional model, irregular sampling, dictionary-based decomposition, non-negativity | Altimeter,Sea surface temperature,Convolution,Interpolation,Remote sensing,Image processing,Sea-surface height,Prior probability,Principal component analysis,Mathematics | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Lopez-Radcenco | 1 | 1 | 1.03 |
| Ronan Fablet | 2 | 3 | 2.84 |
| Abdeldjalil Aïssa-El-Bey | 3 | 162 | 25.10 |
| pierre ailliot | 4 | 20 | 5.50 |