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Informed spatial regularizations for fast fusion of astronomical images

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Abstract

This paper introduces two informed spatial regularizations dedicated to multiband image fusion. The fusion process combines a multispectral image with high spatial resolution and a hyperspectral image with high spectral resolution, with the aim of recovering a full resolution data-cube. In this work, we propose two spatial regularizations that exploit the spatial information of the multispectral image. A weighted Sobolev regularization identifies the sharp structures locations to locally mitigate a smoothness-promoting Sobolev regularization. A dictionary-based regularization takes advantage of spatial redundancy to recover spatial textures using a dictionary learned on the multispectral image. The proposed regularizations are evaluated on realistic simulations of James Webb Space Telescope (JWST) observations of the Orion Bar and show a better reconstruction of sharp structures compared to a non-informed regularization. Since JWST is now in orbit, we expect to use this method on real data in the near future.
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Dates and versions

hal-03724654 , version 1 (15-07-2022)

Identifiers

  • HAL Id : hal-03724654 , version 1

Cite

Claire Guilloteau, Thomas Oberlin, Olivier Berné, Nicolas Dobigeon. Informed spatial regularizations for fast fusion of astronomical images. IEEE International Conference on Image Processing (ICIP 2022), Oct 2022, Bordeaux, France. pp.1-5. ⟨hal-03724654⟩
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