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

Keisuke Ozawa, Shinichi Sumiyoshi, Yusuke Sekikawa, Keisuke Uto, Yuichi Yoshida, Mitsuru Ambai

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.