IEEE Trans Image Process - Compressive sensing of sparse tensors.

Tópicos

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Resumo

Compressive sensing (CS) has triggered an enormous research activity since its first appearance. CS exploits the signal's sparsity or compressibility in a particular domain and integrates data compression and acquisition, thus allowing exact reconstruction through relatively few nonadaptive linear measurements. While conventional CS theory relies on data representation in the form of vectors, many data types in various applications, such as color imaging, video sequences, and multisensor networks, are intrinsically represented by higher order tensors. Application of CS to higher order data representation is typically performed by conversion of the data to very long vectors that must be measured using very large sampling matrices, thus imposing a huge computational and memory burden. In this paper, we propose generalized tensor compressive sensing (GTCS)-a unified framework for CS of higher order tensors, which preserves the intrinsic structure of tensor data with reduced computational complexity at reconstruction. GTCS offers an efficient means for representation of multidimensional data by providing simultaneous acquisition and compression from all tensor modes. In addition, we propound two reconstruction procedures, a serial method and a parallelizable method. We then compare the performance of the proposed method with Kronecker compressive sensing (KCS) and multiway compressive sensing (MWCS). We demonstrate experimentally that GTCS outperforms KCS and MWCS in terms of both reconstruction accuracy (within a range of compression ratios) and processing speed. The major disadvantage of our methods (and of MWCS as well) is that the compression ratios may be worse than that offered by KCS.

Resumo Limpo

compress sens cs trigger enorm research activ sinc first appear cs exploit signal sparsiti compress particular domain integr data compress acquisit thus allow exact reconstruct relat nonadapt linear measur convent cs theori reli data represent form vector mani data type various applic color imag video sequenc multisensor network intrins repres higher order tensor applic cs higher order data represent typic perform convers data long vector must measur use larg sampl matric thus impos huge comput memori burden paper propos general tensor compress sens gtcsa unifi framework cs higher order tensor preserv intrins structur tensor data reduc comput complex reconstruct gtcs offer effici mean represent multidimension data provid simultan acquisit compress tensor mode addit propound two reconstruct procedur serial method paralleliz method compar perform propos method kroneck compress sens kcs multiway compress sens mwcs demonstr experiment gtcs outperform kcs mwcs term reconstruct accuraci within rang compress ratio process speed major disadvantag method mwcs well compress ratio may wors offer kcs

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