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View all- Jin ALi XXiong Q(2023)Security Compressed Sensing Image Encryption Algorithm Based on Elliptic CurveData Science10.1007/978-981-99-5968-6_25(350-360)Online publication date: 15-Sep-2023
Optimization and Reconstruction for EPMA Image Compressed Sensing Based on Chaotic Measurement Matrix
Authors: 
Li Zhang, Jun Zhang, Anan JinAuthors Info & Claims
Li Zhang, Jun Zhang, Anan JinAuthors Info & ClaimsPages 474 - 482
Abstract
Image reconstruction is an important part of today's image processing. The quality and efficiency of image re-construction are one of the research hotspots in today's image processing field. Image reconstruction using compressed sensing can greatly reduce the sampling rate and break the constraint of traditional Nyquist sampling law, which is of great breakthrough significance for image reconstruction. The quality of the measurement matrix in compressed sensing has a great influence on the effect of the reconstruction. Therefore, the construction of the measurement matrix is an important direction of the current research on compressed sensing. This paper is using the deterministic Monte Carlo pseudo-random number sampling method to construct a chaotic measurement matrix for compressed sensing. This measurement matrix can solve the uncertainty of the random matrix. The experimental results show that the reconstruction effect of this method on the EPMA image has a better reconstruction performance than other measurement matrices and achieves the super-resolution recovery.
References
[1]
Donoho D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(5):1289-1306
[2]
Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2):489-509.
[3]
Baraniuk R G. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4):118-121.
[4]
Islam, R., Islam, M. S., & Uddin, M. S. (2021). Compressed Sensing in Parallel MRI: A Review. International Journal of Image and Graphics, 2250038.
[5]
Candes E. Compressive sampling[C]//Proc of International Congress of Mathematicians. 2006:1433-1452.
[6]
Rauhut H. Compressive sensing and structured random matrices[M]. Germany: Theoretical foundations and numerical methods for Sparse Recovery, 2010: 91–92.
[7]
Gilbert A, Indyk P. Sparse recovery using sparse matrices[J]. Proceedings of the IEEE, 2010, 98(6): 937-947.
[8]
Li S, Gao F, Ge G, Deterministic construction of compressed sensing matrices via algebraic curves[J]. IEEE Transactions on Information Theory, 2012, 58(8): 5035–5041.
[9]
Haupt W, Bajwa U, Raz G, Sun H. Toeplitz compressed sensing matrices with applications to sparse channel estimation[J]. IEEE Transactions on Information Theory, 2010, 56(11): 5862–5875.
[10]
Valsesia D, Magli E. Compressive signal processing with circulant sensing matrices [A]. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing[C]. Florence, 2014: 1015-1019.
[11]
Linh-Trung N, Van Phong D, Hussain Z M, et al. Compressed sensing using chaos filters[A]. 2008 Australasian Telecommunication Networks and Applications Conference[C]. Adelaide, 2008: 219-223.
[12]
Yu L, Barbot J P, Zheng G, Toeplitz-structured chaotic sensing matrix for compressive sensing[A]. 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing[C]. Newcastle upon Tyne, 2010: 229-233.
[13]
Yu L, Barbot J P, Zheng G, et al. Compressive sensing with chaotic sequence[J]. IEEE Signal Processing Letters, 2010, 17(8): 731-734.
[14]
Lei Yu, Jean Pierre Barbot, Gang Zheng Compressive Sensing With Chaotic Sequence[J]. IEEE SIGNAL PROCESSING LETTERS, 2010,17(8):731-734.
[15]
Davenport, Mark A., and Michael B. Wakin. "Analysis of orthogonal matching pursuit using the restricted isometry property." IEEE Transactions on Information Theory 56.9 (2010): 4395-4401.
[16]
Fan F. Toeplitz-structured measurement matrix construction for chaotic compressive sensing[A]. Fifth International Conference on Intelligent Control and Information Processing[C]. Dalian, 2014: 19-22.
[17]
Delbari, S. A., Shakeri, M. S., Salahshoori, I., Asl, M. S., Namini, A. S., Abdolmaleki, A., ... & Shokouhimehr, M. (2021). Characterization of TiC ceramics with SiC and/or WC additives using electron microscopy and electron probe micro-analysis. Journal of the Taiwan Institute of Chemical Engineers.
[18]
Tropp, Joel A., and Anna C. Gilbert. "Signal recovery from random measurements via orthogonal matching pursuit." IEEE Transactions on information theory 53.12 (2007): 4655-4666.
[19]
Zhou, Xuan, Songbai Chen, and Jiliang Jing. "Chaotic motion of scalar particle coupling to Chern–Simons invariant in Kerr black hole spacetime." The European Physical Journal C 81, no. 3 (2021): 1-14.
[20]
Niyat, Abolfazl Yaghouti, and Mohammad Hossein Moattar. "Color image encryption based on hybrid chaotic system and DNA sequences." Multimedia Tools and Applications 79.1 (2020): 1497-1518.
[21]
Tanveer, M., Shah, T., Ali, A., & Shah, D. (2021). An Efficient Image Privacy-Preserving Scheme Based On Mixed Chaotic Map and Compression. International Journal of Image and Graphics, 2250020.
[22]
LEI Y, BARBOT J P, GANG Z, Compressive sensing with chaotic sequence[J]. IEEE Signal Processing Letters, 2010, 17(8): 731-734.
[23]
Frunzete, M., Yu, L., Barbot, J. P., & Vlad, A. (2011, September). Compressive sensing matrix designed by tent map, for secure data transmission. In Signal Processing Algorithms, Architectures, Arrangements, and Applications SPA 2011 (pp. 1-6). IEEE.
[24]
Gao, Xiaohong. "A color image encryption algorithm based on an improved Hénon map." Physica Scripta 96, no. 6 (2021): 065203.
[25]
Deng, Jie, Minjun Zhou, Chunhua Wang, Sicheng Wang, and Cong Xu. "Image segmentation encryption algorithm with chaotic sequence generation participated by cipher and multi-feedback loops." Multimedia Tools and Applications 80, no. 9 (2021): 13821-13840.
[26]
Krupenev, Dmitry, Denis Boyarkin, and Dmitrii Iakubovskii. "Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method." Reliability Engineering & System Safety 204 (2020): 107171.
[27]
Golder, E. R., and J. G. Settle. "The Box‐Müller Method for Generating Pseudo‐Random Normal Deviates." Journal of the Royal Statistical Society: Series C (Applied Statistics) 25.1 (1976): 12-20.
[28]
Dubi A. Monte Carlo applications in systems engineering [J]. A Dubi Stochasti Modeling of Realistic Systems Topics in Reliability & Maintainability & Statistics Tutorial Notes IEEE R & M Symposium,1999.
[29]
Blumensath T, Davies M. Iterative hard thresholding for compressed sensing [J]. Applied and Computational Harmonic Analysis. 2009, 27(3): 265–274.
[30]
Gupta, S. C., and V. K. Kapoor. Fundamentals of mathematical statistics. Sultan Chand & Sons, 2020.
[31]
Cands E, Tao T. Decoding by linear programming [J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.
[32]
Candes, Emmanuel J., Justin K. Romberg, and Terence Tao. "Stable signal recovery from incomplete and inaccurate measurements." Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences 59.8 (2006): 1207-1223.
[33]
Candès, E. J., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on information theory, 52(2), 489-509.
[34]
Chen, Jian, Yan Li, and LiHua Cao. "Research on region selection super resolution restoration algorithm based on infrared micro-scanning optical imaging model." Scientific Reports 11, no. 1 (2021): 1-8.
[35]
Han, Ming, and Han Liu. "Super-resolution restoration of degraded image based on fuzzy enhancement." Arabian Journal of Geosciences 14, no. 11 (2021): 1-7.
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February 2022
570 pages
ISBN:9781450395700
DOI:10.1145/3529836
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Published: 21 June 2022
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- Research
- Refereed limited
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- Project of Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology
- National Natural Science Foundation of China
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ICMLC 2022
ICMLC 2022: 2022 14th International Conference on Machine Learning and Computing
February 18 - 21, 2022
Guangzhou, China
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View all- Jin ALi XXiong Q(2023)Security Compressed Sensing Image Encryption Algorithm Based on Elliptic CurveData Science10.1007/978-981-99-5968-6_25(350-360)Online publication date: 15-Sep-2023
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