Snapshot Multispectral Image Completion Via Self-Dictionary Transformed Tensor Nuclear Norm Minimization With Total Variation

Snapshot multispectral imaging suffers from severely low spatial resolution and degraded signals due to mosaic rearrangement. In order to recover a signal of full bands and full sensor size from a single snapshot, we propose a self-dictionary-transformed tensor nuclear norm and develop a joint optim...

Full description

Saved in:
Bibliographic Details
Published in2021 IEEE International Conference on Image Processing (ICIP) pp. 364 - 368
Main Authors Ozawa, Keisuke, Sumiyoshi, Shinichi, Sekikawa, Yusuke, Uto, Keisuke, Yoshida, Yuichi, Ambai, Mitsuru
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.09.2021
Subjects
convex optimization
data completion
Data models
Minimization
Multispectral image processing
Multispectral imaging
Numerical models
Reflectivity
tensor decomposition
Tensors
Visualization
Online AccessGet full text

Cover

More Information
Summary:Snapshot multispectral imaging suffers from severely low spatial resolution and degraded signals due to mosaic rearrangement. In order to recover a signal of full bands and full sensor size from a single snapshot, we propose a self-dictionary-transformed tensor nuclear norm and develop a joint optimization with total variation regularization as a convex completion problem. The proposed nuclear norm is designed specifically for the intrinsic structure of snapshot multispectral data, reflects the parsimony of tensors that conventional approaches ignore, as well as incorporates inter-axis correlation unlike matrix-based optimization. We show increased accuracy with our self-dictionary throughout simulation experiments and demonstrate quality enhancement in recovering real snapshot multispectral images.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506547