Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data
This work addresses image enhancement for ocean remote sensing applications, but it is incremental as it builds on existing super-resolution and interpolation methods.
The paper tackled super-resolution of irregularly-sampled ocean remote sensing data, such as sea surface height from altimeter and temperature data, by using locally-adapted convolutional models with non-negativity constraints, which outperformed optimal interpolation reconstructions in experiments.
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.