IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v019i01.html
   My bibliography  Save this article

Adaptive Smoothing of Digital Images: The R Package adimpro

Author

Listed:
  • Polzehl, Jörg
  • Tabelow, Karsten

Abstract

Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used non-adaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the propagationseparation approach by (Polzehl and Spokoiny 2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples.

Suggested Citation

  • Polzehl, Jörg & Tabelow, Karsten, 2007. "Adaptive Smoothing of Digital Images: The R Package adimpro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i01).
    • Handle: RePEc:jss:jstsof:v:019:i01
    DOI: http://hdl.handle.net/10.18637/jss.v019.i01
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v019i01/v19i01.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v019i01/adimpro_0.4.2.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v019i01/Examples.R.zip
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v019.i01?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. Jörg Polzehl & Vladimir G. Spokoiny, 2001. "Functional and dynamic magnetic resonance imaging using vector adaptive weights smoothing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 485-501.
    2. J. Polzehl & V. G. Spokoiny, 2000. "Adaptive weights smoothing with applications to image restoration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 335-354.
    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. repec:jss:jstsof:31:i09 is not listed on IDEAS
    2. repec:jss:jstsof:44:i12 is not listed on IDEAS
    3. Fiebig, Ewelina Marta, 2021. "On data-driven choice of λ in nonparametric Gaussian regression via Propagation–Separation approach," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    4. Polzehl, Jörg & Tabelow, Karsten, 2009. "Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i09).

    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. repec:jss:jstsof:19:i01 is not listed on IDEAS
    2. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    3. Geffray, S. & Klutchnikoff, N. & Vimond, M., 2016. "Illumination problems in digital images. A statistical point of view," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 191-213.
    4. Čížek, Pavel & Koo, Chao Hui, 2021. "Jump-preserving varying-coefficient models for nonlinear time series," Econometrics and Statistics, Elsevier, vol. 19(C), pages 58-96.
    5. Billé, AG & Salvioni, C. & Benedetti, R., 2015. "Spatial Heterogeneity In Production Functions Models," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212662, European Association of Agricultural Economists.
    6. Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2015. "Identifying Berlin’s land value map using adaptive weights smoothing," Computational Statistics, Springer, vol. 30(3), pages 767-790, September.
    7. Vyacheslav Abramov & Fima Klebaner, 2007. "Estimation and Prediction of a Non-Constant Volatility," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(1), pages 1-23, March.
    8. J. Polzehl & V. Spokoiny & C. Starica, 2004. "When did the 2001 recession really start?," Econometrics 0411017, University Library of Munich, Germany.
    9. Meise, Monika & Davies, Paul Lyndon, 2005. "Approximating data with weighted smoothing splines," Technical Reports 2005,48, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    10. Timmermans, Catherine & Fryzlewicz, Piotr, 2012. "Shah: Shape-Adaptive Haar Wavelet Transform For Images With Application To Classification," LIDAM Discussion Papers ISBA 2012015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Peihua Qiu, 2009. "Jump-preserving surface reconstruction from noisy data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 715-751, September.
    12. Helbing, Georg & Shen, Zhiwei & Odening, Martin & Ritter, Matthias, 2017. "Estimating Location Values of Agricultural Land," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 66(3), September.
    13. Qiu, Peihua, 2008. "A nonparametric procedure for blind image deblurring," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4828-4841, June.
    14. M. Simona Andreano & Roberto Benedetti & Paolo Postiglione, 2017. "Spatial regimes in regional European growth: an iterated spatially weighted regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2665-2684, November.
    15. Catalin Starica & Stefano Herzel & Tomas Nord, 2005. "Why does the GARCH(1,1) model fail to provide sensible longer- horizon volatility forecasts?," Econometrics 0508003, University Library of Munich, Germany.
    16. Jörg Polzehl & Vladimir Spokoiny, 2006. "Varying coefficient GARCH versus local constant volatility modeling. Comparison of the predictive power," SFB 649 Discussion Papers SFB649DP2006-033, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    17. Sadick Mohammed & Awudu Abdulai, 2022. "Do Egocentric information networks influence technical efficiency of farmers? Empirical evidence from Ghana," Journal of Productivity Analysis, Springer, vol. 58(2), pages 109-128, December.
    18. Lei Xu & Timothy D. Johnson & Thomas E. Nichols & Derek E. Nee, 2009. "Modeling Inter-Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model," Biometrics, The International Biometric Society, vol. 65(4), pages 1041-1051, December.
    19. Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2012. "Location, Location, Location: Extracting Location Value from House Prices," Discussion Papers of DIW Berlin 1216, DIW Berlin, German Institute for Economic Research.
    20. Anna Gloria Billé & Roberto Benedetti & Paolo Postiglione, 2017. "A two-step approach to account for unobserved spatial heterogeneity," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(4), pages 452-471, October.
    21. Hongtu Zhu & Jianqing Fan & Linglong Kong, 2014. "Spatially Varying Coefficient Model for Neuroimaging Data With Jump Discontinuities," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1084-1098, September.

    More about this item

    Statistics

    Access and download statistics

    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:jss:jstsof:v:019:i01. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    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.