rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Blind Image Deconvolution
    sub title: Methods and Convergence
  name:
    name part: Chaudhuri, Subhasis.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Velmurugan, Rajbabu.
    role:
      role term: author.
  name:
    name part: Rameshan, Renu.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 151 p. 33 illus., 16 illus. in color. online resource.
  abstract: Blind deconvolution is a classical image processing problem which has been investigated by a large number of researchers over the last four decades. The purpose of this monograph is not to propose yet another method for blind image restoration. Rather the basic issue of deconvolvability has been explored from a theoretical view point. Some authors claim very good results while quite a few claim that blind restoration does not work. The authors clearly detail when such methods are expected to work and when they will not. In order to avoid the assumptions needed for convergence analysis in the Fourier domain, the authors use a general method of convergence analysis used for alternate minimization based on three point and four point properties of the points in the image space. The authors prove that all points in the image space satisfy the three point property and also derive the conditions under which four point property is satisfied. This provides the conditions under which alternate minimization for blind deconvolution converges with a quadratic prior. Since the convergence properties depend on the chosen priors, one should design priors that avoid trivial solutions. Hence, a sparsity based solution is also provided for blind deconvolution, by using image priors having a cost that increases with the amount of blur, which is another way to prevent trivial solutions in joint estimation. This book will be a highly useful resource to the researchers and academicians in the specific area of blind deconvolution.
  table of contents: Introduction -- Mathematical Background -- Blind Deconvolution Methods: A Review -- MAP Estimation: When Does it Work? -- Convergence Analysis in Fourier Domain -- Spatial Domain Convergence Analysis -- Sparsity-based Blind Deconvolution -- Conclusions and Future Research Directions.
  note: by Subhasis Chaudhuri, Rajbabu Velmurugan, Renu Rameshan.
  subject:
    topic: Computer science
  subject:
    topic: Image processing
  subject:
    topic: Computer Science
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Signal, Image and Speech Processing
  classification: TA1637-1638
  classification: TA1634
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319104850
  identifier: http://dx.doi.org/10.1007/978-3-319-10485-0
  record info:
    record creation date: 140922
    record change date: 20151204165604.0
    record identifier: 978-3-319-10485-0