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Joint demosaicing and fusion of multiresolution coded acquisitions: A unified image formation and reconstruction method

Authors :
Picone, Daniele
Mura, Mauro Dalla
Condat, Laurent
Source :
IEEE Transactions on Computational Imaging, Vol. 9 (2023), p. 335-349
Publication Year :
2022

Abstract

Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by a single focal plane array. In this work, we propose to model the capturing system of a multiresolution coded acquisition (MRCA) in a unified framework, which natively includes conventional systems such as those based on spectral/color filter arrays, compressed coded apertures, and multiresolution sensing. We also propose a model-based image reconstruction algorithm performing a joint demosaicing and fusion (JoDeFu) of any acquisition modeled in the MRCA framework. The JoDeFu reconstruction algorithm solves an inverse problem with a proximal splitting technique and is able to reconstruct an uncompressed image datacube at the highest available spatial and spectral resolution. An implementation of the code is available at https://github.com/danaroth83/jodefu.<br />Comment: 15 pages, 7 figures; regular paper

Details

Database :
arXiv
Journal :
IEEE Transactions on Computational Imaging, Vol. 9 (2023), p. 335-349
Publication Type :
Report
Accession number :
edsarx.2209.01455
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/TCI.2023.3261503