publications:
- title: "Total Variation Applications in Computer Vision"
  author:
  - name: "Vania Vieira Estrela"
    link: "http://buscatextual.cnpq.br/buscatextual/visualizacv.jsp?id=K4793789Y3&mostrarNroCitacoesISI=true&mostrarNroCitacoesScopus=true&mostrarNroCitacoesScielo=true"
  - name: "Hermes Magalhaes"
    link: "http://lattes.cnpq.br/2691917174187171"
  - name: "Osamu Saotome"
    link: "http://academic.research.microsoft.com/Author/3476641/osamu-saotome?query=osamu%20saotome"
  year: "2016"
  month: "mar"
  abstract: "The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observed and the original images. The regularization scalar parameter λ controls the amount of regularization allowed and it is an essential to obtain a high-quality regularized output. A wide-ranging review of several ways to put into practice TV regularization as well as its advantages and limitations are discussed. "
  researchr: "https://researchr.org/publication/2016arXiv160309599E"
  cites: 126
  citedby: 1
  journal: "ArXiv e-prints"
  kind: "article"
  key: "2016arXiv160309599E"