TY - JOUR
T1 - Joint Demosaicing and Fusion of Multiresolution Coded Acquisitions: An Unified Image Formation and Reconstruction Method
AU - Picone, Daniele
AU - Dalla Mura, Mauro
AU - Condat, Laurent Pierre
N1 - KAUST Repository Item: Exported on 2023-03-27
Acknowledgements: This work is partly supported by grant ANR FuMultiSPOC (ANR-20-ASTR-0006).
PY - 2023/3/24
Y1 - 2023/3/24
N2 - Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by the same focal plane array. In this work, we propose to model a multiresolution coded acquisition (MRCA) in a generic framework, which natively includes acquisitions by conventional systems such as those based on spectral/color filter arrays, compressed coded apertures, and multiresolution sensing. We 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.
AB - Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by the same focal plane array. In this work, we propose to model a multiresolution coded acquisition (MRCA) in a generic framework, which natively includes acquisitions by conventional systems such as those based on spectral/color filter arrays, compressed coded apertures, and multiresolution sensing. We 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.
UR - http://hdl.handle.net/10754/690585
UR - https://ieeexplore.ieee.org/document/10080979/
U2 - 10.1109/tci.2023.3261503
DO - 10.1109/tci.2023.3261503
M3 - Article
SN - 2333-9403
SP - 1
EP - 15
JO - IEEE Transactions on Computational Imaging
JF - IEEE Transactions on Computational Imaging
ER -