Compressive multi-spectral imaging using self-correlations of images based on hierarchical joint sparsity models

Daisuke Sugimura, Masaru Tomabechi, Tadaaki Hosaka, Takayuki Hamamoto

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We propose a novel multi-spectral imaging method based on compressive sensing (CS). In CS theory, the enhancement of signal sparsity is important for accurate signal reconstruction. The main novelty of the proposed method is the employment of a self-correlation of an image, that is a local intensity similarity and multi-spectral correlation, to enhance the sparsity of the multi-spectral image to be recovered. Local intensity similarity, which is based on the concept that spatial changes in intensity are likely to be similar within local regions, contributes to sparsity enhancement. Furthermore, we exploit multi-spectral correlation to improve the sparsity of the multi-spectral components to be recovered. In order to simultaneously exploit different types of characteristics (i.e., local intensity similarity and multi-spectral correlation) for representing a signal as sufficiently sparse, we introduce a hierarchical joint sparsity model in the CS image recovery process. Our experiments show that the use of a self-correlation significantly improves the performance of multi-spectral image reconstruction.

Original languageEnglish
Pages (from-to)499-510
Number of pages12
JournalMachine Vision and Applications
Volume27
Issue number4
DOIs
Publication statusPublished - 1 May 2016

Keywords

  • Compressive sensing
  • Hierarchical joint sparsity models
  • Multi-spectral imaging
  • Self-correlation of images

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