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.
@article{arxiv.1704.02162,
title = {Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data},
author = {Manuel López-Radcenco and Ronan Fablet and Abdeldjalil Aïssa-El-Bey and Pierre Ailliot},
journal= {arXiv preprint arXiv:1704.02162},
year = {2017}
}