Open AccessView's dependency and low‐rank background‐guided compressed sensing for multi‐view image joint reconstruction
Author(s) -
Fei Xuan,
Li Lei,
Cao Heling,
Miao Jianyu,
Yu Renping
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0295
Subject(s) - computer science , artificial intelligence , iterative reconstruction , compressed sensing , computer vision , parametric statistics , dependency (uml) , image (mathematics) , transformation (genetics) , fidelity , joint (building) , rank (graph theory) , field (mathematics) , construct (python library) , algorithm , regular polygon , pattern recognition (psychology) , mathematics , architectural engineering , telecommunications , biochemistry , statistics , chemistry , combinatorics , pure mathematics , engineering , gene , programming language , geometry
Compressed sensing (CS) multi‐camera network reconstruction has attracted much attention in the field of distributed CS networks. However, many multi‐camera network reconstructions based on CS usually recover every image separately; the view's dependency and geometrical structure among these multi‐view images could be rarely considered in this way, which will result in some unsatisfied joint reconstruction results. Here, the authors introduce to extract the multiple view geometry from multi‐view images to construct the view's dependency observation model. Based on the proposed parametric transformation observation model, they propose a novel CS joint reconstruction method of multi‐view image that guided by the spatial correlation and low‐rank background constraints. The eventual optimisation model could be relaxed to a series of convex optimisation problems, which could be efficiently solved by combining the variable splitting and alternate iteration technique. The extended experimental results indicate that they proposed method has achieved a remarkable improvement in both objective criterion and visual fidelity compared with other competitive reconstruction methods.