From Compressed Sensing to Low-rank Matrix Recovery: Theory and Applications

Peng, Yigang; Suo, Jinli; Dai, Qionghai; Wang, Xu

doi:10.1016/s1874-1029(13)60063-4

Cited by 11 publications

(1 citation statement)

References 39 publications

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“…Recently, low‐rank‐based methods has been developed to further exploit temporal sparsity. Peng et al [15] review the fundamental theories about CS, matrix rank minimisation, and low‐rank matrix recovery, and then introduce the typical applications of these theories in image processing, computer vision, and computational photography. They also propose a robust alignment algorithm by sparse and low‐rank decomposition for linearly correlated images [16].…”

Section: Related Workmentioning

confidence: 99%

View's dependency and low‐rank background‐guided compressed sensing for multi‐view image joint reconstruction

Xuan

Cao

et al. 2019

IET image process

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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.

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Section: Related Workmentioning

confidence: 99%