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: Handbook of Convex Optimization Methods in Imaging Science
  name:
    name part: Monga, Vishal.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVII, 228 p. 83 illus., 50 illus. in color. online resource.
  abstract: This book covers recent advances in image processing and imaging sciences from an optimization viewpoint, especially convex optimization with the goal of designing tractable algorithms. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems. The first part includes a review of the mathematical methods and foundations required, and covers topics in image quality optimization and assessment. The second part of the book discusses concepts in image formation and capture from color imaging to radar and multispectral imaging. The third part focuses on sparsity constrained optimization in image processing and vision and includes inverse problems such as image restoration and de-noising, image classification and recognition and learning-based problems pertinent to image understanding. Throughout, convex optimization techniques are shown to be a critically important mathematical tool for imaging science problems and applied extensively. Convex Optimization Methods in Imaging Science is the first book of its kind and will appeal to undergraduate and graduate students, industrial researchers and engineers and those generally interested in computational aspects of modern, real-world imaging and image processing problems. Discusses recent developments in imaging science and provides tools for solving image processing and computer vision problems using convex optimization methods. The reader is provided with the state of the art advancements in each imaging science problem that is covered and is directed to cutting edge theory and methods that should particularly help graduate students and young researchers in shaping their research. Each chapter of the book covers a real-world imaging science problem while balancing both the theoretical and experimental aspects. The theoretical foundation of the problem is discussed thoroughly and then from a p…
  table of contents: Preface -- 1 Introduction -- 2 Optimizing Image Quality -- 3 Computational Color Imaging -- 4 Optimization Methods for SAR -- 5 Computational Spectral Ultrafast Imaging -- 6 Discriminative Sparse Representation -- 7 Sparsity-based Nonlocal Image Restoration -- 8 Sparsity Constrained Estimation -- 9 Optimization Problems Associated with Manifolds.
  note: edited by Vishal Monga.
  subject:
    topic: Optical data processing
  subject:
    topic: Signal processing
  subject:
    topic: Image processing
  subject:
    topic: Speech processing systems
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Signal, Image and Speech Processing
  classification: TA1630-1650
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319616094
  identifier: https://doi.org/10.1007/978-3-319-61609-4
  record info:
    record creation date: 171027
    record change date: 20191027162109.0
    record identifier: 978-3-319-61609-4