2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-MDSP.11
Paper Title SNAPSHOT MULTISPECTRAL COMPLETION VIA SELF-DICTIONARY TRANSFORMED TENSOR NUCLEAR NORM MINIMIZATION WITH TOTAL VARIATION
Authors Keisuke Ozawa, Shinichi Sumiyoshi, Yusuke Sekikawa, Keisuke Uto, Yuichi Yoshida, Mitsuru Ambai, DENSO IT Laboratory, Japan
SessionMLR-APPL-MDSP: Machine learning for multidimensional signal processing
LocationArea F
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Multidimensional Signal Processing: Spectral estimation
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Abstract 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.