Compressive sensing using chaotic sequence based on Chebyshev map

Gan, Hongping; Li, Zhi; Li, Jian; Wang, Xi; Zheng-fu, Cheng

doi:10.1007/s11071-014-1600-1

Cited by 43 publications

(15 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to a Gaussian matrix, a Bernoulli matrix or a random discrete Fourier transform matrix can also be used as measurement matrix [30]. The typical representative of deterministic construction method is chaotic matrices [31]- [33]. Yu et al have proven that their constructed Logistic matrices satisfy the RIP with overwhelming probability if the sparsity k ≤ O(m/ log(n/k)).…”

Section: Premilinaries a Compressed Sensingmentioning

confidence: 99%

Public Key Image Encryption Based on Compressed Sensing

Xie

2019

IEEE Access

View full text Add to dashboard Cite

The compression feature of compressed sensing (CS) enables that the ciphertext lengths of multimedia encryption schemes have reduced dramatically, and the simple linear measurements make the encryption process extremely efficient. Therefore, CS based image encryption schemes have attached wide attentions in recent years. However, almost all CS based image encryption schemes follow the framework of symmetric encryption. In this paper, we propose the first CS based public key image encryption scheme PKIE-CS with accurate decryption. As is known to all, two important conditions for successful signal reconstruction in CS are signal sparsity and measurement matrix satisfying some properties (e.g., restricted isometry property). In our scheme, the relationship between the public key and the secret key can be expressed by B P [T; I] = A T P, where B P and A are public parameters, T is the secret key, and B P can not be used as the measurement matrix in CS. Thus, anyone can not extract the secret key T from public key. We first give the specific descriptions of PKIE-CS and then give some theoretical analysis for providing strong security evidence. Last, the experimental results and the comparison with similar works demonstrate the feasibility and the superiority of the proposed scheme. INDEX TERMS Compressed sensing, cipher-only attack, correlation analysis, public key image encryption.

show abstract

Section: Premilinaries a Compressed Sensingmentioning

confidence: 99%

Public Key Image Encryption Based on Compressed Sensing

Xie

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Both RIP [16][17][18][19] and coherence [9][10][11][12][13][20][21][22][23][24][25][26][27] are important tools to analyze the property of measurement matrices. In this paper, coherence will be adopted to analyze and illustrate the property of constructed measurement matrices, because it is easier to compute.…”

Section: Lemma 11mentioning

confidence: 99%

Binary Sequence Family for Chaotic Compressed Sensing

2019

KSII TIIS

View full text Add to dashboard Cite

It is significant to construct deterministic measurement matrices with easy hardware implementation, good sensing performance and good cryptographic property for practical compressed sensing (CS) applications. In this paper, a deterministic construction method of bipolar chaotic measurement matrices is presented based on binary sequence family (BSF) and Chebyshev chaotic sequence. The column vectors of these matrices are the sequences of BSF, where 1 is substituted with-1 and 0 is with 1. The proposed matrices, which exploit the pseudo-randomness of Chebyshev sequence, are sensitive to the initial state. The performance of proposed matrices is analyzed from the perspective of coherence. Theoretical analysis and simulation experiments show that the proposed matrices have limited influence on the recovery accuracy in different initial states and they outperform their Gaussian and Bernoulli counterparts in recovery accuracy. The proposed matrices can make the hardware implement easy by means of linear feedback shift register (LFSR) structures and numeric converter, which is conducive to practical CS.

show abstract

“…The column vectors of the measurement matrix are generated by cyclically shifting a logistic chaotic pseudorandom sequence. H. Gan et al [15] proposed the deterministic measurement matrix by using chebyshev chaotic pseudorandom sequence. M. Frunzete et al [16] proposed the deterministic measurement matrix by using tent chaotic sequence.…”

Section: Introductionmentioning

confidence: 99%

A Simple Deterministic Measurement Matrix Based on GMW Pseudorandom Sequence

Liu

Hua

Yin

et al. 2019

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Compressed sensing is an effective compression algorithm. It is widely used to measure signals in distributed sensor networks (DSNs). Considering the limited resources of DSNs, the measurement matrices used in DSNs must be simple. In this paper, we construct a deterministic measurement matrix based on Gordon-Mills-Welch (GMW) sequence. The column vectors of the proposed measurement matrix are generated by cyclically shifting a GMW sequence. Compared with some state-of-theart measurement matrices, the proposed measurement matrix has relative lower computational complexity and needs less storage space. It is suitable for resource-constrained DSNs. Moreover, because the proposed measurement matrix can be realized by using simple shift register, it is more practical. The simulation result shows that, in terms of recovery quality, the proposed measurement matrix performs better than some state-of-the-art measurement matrices.

show abstract

Compressive sensing using chaotic sequence based on Chebyshev map

Cited by 43 publications

References 26 publications

Public Key Image Encryption Based on Compressed Sensing

Public Key Image Encryption Based on Compressed Sensing

Binary Sequence Family for Chaotic Compressed Sensing

A Simple Deterministic Measurement Matrix Based on GMW Pseudorandom Sequence

Contact Info

Product

Resources

About