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Adaptive Morphologic Regularizations for Inverse Problems

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Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7883))

Abstract

Regularization is an well-known technique for obtaining stable solution of ill-posed inverse problems. In this paper we establish a key relationship among the regularization methods with edge-preserving noise filtering method which leads to an efficient adaptive regularization methods. We show experimentally the efficiency and superiority of the proposed regularization methods for some inverse problems, e.g. deblurring and super-resolution (SR) image reconstruction.

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Author information

Authors and Affiliations

  1. ECSU, Indian Statistical Institute, India

    Pulak Purkait & Bhabatosh Chanda

Authors
  1. Pulak Purkait
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  2. Bhabatosh Chanda
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Editor information

Editors and Affiliations

  1. Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Box 337, 751 05, Uppsala, Sweden

    Cris L. Luengo Hendriks , Gunilla Borgefors  & Robin Strand ,  & 

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Purkait, P., Chanda, B. (2013). Adaptive Morphologic Regularizations for Inverse Problems. In: Hendriks, C.L.L., Borgefors, G., Strand, R. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2013. Lecture Notes in Computer Science, vol 7883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38294-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-38294-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38293-2

  • Online ISBN: 978-3-642-38294-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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