# Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data

## TL;DR

This paper proposes a locally-adapted convolutional super-resolution method for irregularly-sampled ocean remote sensing data, improving reconstruction quality over traditional interpolation techniques.

## Contribution

It introduces a novel locally-adapted multimodal convolutional model with dictionary decompositions for super-resolution of irregular remote sensing data.

## Key findings

- Locally-adapted models outperform optimal interpolation.
- Non-negativity constraints improve reconstruction accuracy.
- Method effectively reconstructs sea surface height from multiple data sources.

## 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.