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Communication Dans Un Congrès Année : 2017

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

Résumé

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.
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Dates et versions

hal-01735256 , version 1 (15-03-2018)
hal-01735256 , version 2 (05-09-2018)

Identifiants

Citer

Manuel Lopez Radcenco, Ronan Fablet, Abdeldjalil Aissa El Bey, Pierre Ailliot. Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data. ICIP 2017 : IEEE International Conference on Image Processing, Sep 2017, Beijing, China. ⟨10.1109/ICIP.2017.8297095⟩. ⟨hal-01735256v2⟩
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