Open AccessImage compressive sensing reconstruction based on collaboration reduced rank preprocessing
Author(s) -
Tan Yun,
Hou Xingsong,
Chen Zan,
Yu Shihang
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.3179
Subject(s) - preprocessor , rank (graph theory) , compressed sensing , image (mathematics) , computer science , iterative reconstruction , artificial intelligence , property (philosophy) , pattern recognition (psychology) , data pre processing , algorithm , computer vision , mathematics , combinatorics , philosophy , epistemology
The problem of image compressive sensing (CS) preprocessing is considered. Currently, image CS reconstruction algorithms mainly consider the sparsity prior knowledge of original image. However, the change of the sparsity strength among the different images may degrade the efficiency of the reconstruction algorithms. Thus the idea of CS preprocessing is proposed to serve two purposes: strengthen the sparsity property of the CS measured image and make preprocessing and reconstruction algorithm matched. Specifically, the collaboration reduced rank (CRR) preprocessing is proposed based on non‐local sparsity and non‐local low‐rank regularisation reconstruction algorithm (NLR‐CS). Then a more efficient CRR‐NLR‐CS CS reconstruction method is proposed which utilises the CRR preprocessing and NLR‐CS. Experimental results show the effectiveness of the proposed method.