IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v14y2018i6p1550147718781751.html
   My bibliography  Save this article

Adaptive compressive sensing of images using error between blocks

Author

Listed:
  • Ran Li
  • Xiaomeng Duan
  • Yongfeng Lv

Abstract

Block compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks. First, we divide image into several non-overlapped blocks and compute the errors between each block and its adjacent blocks. Then, the error between blocks is used to measure the structure complexity of each block, and the measurement rate of each block is adaptively determined based on the distribution of these errors. Finally, we reconstruct each block using a linear model. Experimental results show that the proposed adaptive block compressive sensing system improves the qualities of reconstructed images from both subjective and objective points of view when compared with image block compressive sensing system.

Suggested Citation

  • Ran Li & Xiaomeng Duan & Yongfeng Lv, 2018. "Adaptive compressive sensing of images using error between blocks," International Journal of Distributed Sensor Networks, , vol. 14(6), pages 15501477187, June.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:6:p:1550147718781751
    DOI: 10.1177/1550147718781751
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147718781751
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147718781751?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Marsaglia, George & Tsang, Wai Wan, 2000. "The Ziggurat Method for Generating Random Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 5(i08).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dibyalekha Nayak & Kananbala Ray & Tejaswini Kar & Sachi Nandan Mohanty, 2023. "Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application," Mathematics, MDPI, vol. 11(7), pages 1-21, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Parrini, Alessandro, 2013. "Importance Sampling for Portfolio Credit Risk in Factor Copula Models," MPRA Paper 103745, University Library of Munich, Germany.
    2. Björn Lutz, 2010. "Pricing of Derivatives on Mean-Reverting Assets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-02909-7, December.
    3. repec:jss:jstsof:12:i07 is not listed on IDEAS
    4. Kurita, Takamitsu, 2020. "Likelihood-based tests for parameter constancy in I(2) CVAR models with an application to fixed-term deposit data," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    5. Nordahl, Helge A., 2008. "Valuation of life insurance surrender and exchange options," Insurance: Mathematics and Economics, Elsevier, vol. 42(3), pages 909-919, June.
    6. Michele Azzone & Roberto Baviera, 2023. "A fast Monte Carlo scheme for additive processes and option pricing," Computational Management Science, Springer, vol. 20(1), pages 1-34, December.
    7. Hime Aguiar e Oliveira, 2022. "Deterministic sampling from uniform distributions with Sierpiński space-filling curves," Computational Statistics, Springer, vol. 37(1), pages 535-549, March.
    8. Diaz-Emparanza, Ignacio, 2014. "Numerical distribution functions for seasonal unit root tests," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 237-247.
    9. Roberto Baviera & Pietro Manzoni, 2024. "Fast and General Simulation of L\'evy-driven OU processes for Energy Derivatives," Papers 2401.15483, arXiv.org.
    10. Ömür Ugur, 2008. "An Introduction to Computational Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number p556, February.
    11. Emma Viviani & Luca Di Persio & Matthias Ehrhardt, 2021. "Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case," Energies, MDPI, vol. 14(2), pages 1-33, January.
    12. Thomas W. Zuehlke, 2017. "Use of quadratic terms in Type 2 Tobit models," Applied Economics, Taylor & Francis Journals, vol. 49(17), pages 1706-1714, April.
    13. Huthmacher, Klaus & Herzwurm, André & Gnewuch, Michael & Ritter, Klaus & Rethfeld, Baerbel, 2015. "Monte Carlo simulation of electron dynamics in liquid water," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 242-251.
    14. Yiran Chen & Giray Ökten, 2022. "A goodness-of-fit test for copulas based on the collision test," Statistical Papers, Springer, vol. 63(5), pages 1369-1385, October.
    15. Rui Zhang & Lawrence M. Leemis, 2012. "Rectangles algorithm for generating normal variates," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(1), pages 52-57, February.
    16. Ahmed Bensaida, 2012. "Improving the Forecasting Power of Volatility Models," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 2(3), pages 51-64, July.
    17. Nguyen Nguyet & Xu Linlin & Ökten Giray, 2018. "A quasi-Monte Carlo implementation of the ziggurat method," Monte Carlo Methods and Applications, De Gruyter, vol. 24(2), pages 93-99, June.
    18. Harman, Radoslav & Lacko, Vladimír, 2010. "On decompositional algorithms for uniform sampling from n-spheres and n-balls," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2297-2304, November.
    19. Allin Cottrell, 2021. "Response surfaces for DF-GLS p-values," gretl working papers 8, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    20. Michele Azzone & Roberto Baviera, 2021. "A fast Monte Carlo scheme for additive processes and option pricing," Papers 2112.08291, arXiv.org, revised Jul 2023.
    21. Leong, Philip H. W. & Zhang, Ganglie & Lee, Dong-U & Luk, Wayne & Villasenor, John, 2005. "A Comment on the Implementation of the Ziggurat Method," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i07).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:14:y:2018:i:6:p:1550147718781751. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.