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Informed spatial regularizations for fast fusion of astronomical images

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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Contributor : Nicolas Dobigeon Connect in order to contact the contributor
Submitted on : Friday, July 15, 2022 - 3:57:48 PM
Last modification on : Wednesday, November 16, 2022 - 11:46:09 AM
Long-term archiving on: : Monday, October 17, 2022 - 2:56:40 PM


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  • HAL Id : hal-03724654, version 1


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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