TY - JOUR
T1 - Compressive multi-spectral imaging using self-correlations of images based on hierarchical joint sparsity models
AU - Sugimura, Daisuke
AU - Tomabechi, Masaru
AU - Hosaka, Tadaaki
AU - Hamamoto, Takayuki
PY - 2016/5/1
Y1 - 2016/5/1
N2 - 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.
AB - 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.
KW - Compressive sensing
KW - Hierarchical joint sparsity models
KW - Multi-spectral imaging
KW - Self-correlation of images
UR - http://www.scopus.com/inward/record.url?scp=84962659346&partnerID=8YFLogxK
U2 - 10.1007/s00138-016-0761-y
DO - 10.1007/s00138-016-0761-y
M3 - Article
AN - SCOPUS:84962659346
VL - 27
SP - 499
EP - 510
JO - Machine Vision and Applications
JF - Machine Vision and Applications
SN - 0932-8092
IS - 4
ER -