Algorithms and software for total variation image reconstruction via first-order methods. This paper describes new algorithms and related software for total variation (TV) image reconstruction, more specifically: denoising, inpainting, and deblurring. The algorithms are based on one of Nesterov’s first-order methods, tailored to the image processing applications in such a way that, except for the mandatory regularization parameter, the user needs not specify any parameters in the algorithms. The software is written in C with interface to Matlab (version 7.5 or later), and we demonstrate its performance and use with examples. (netlib numeralgo na28)

References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Li, Yusheng; Xie, Xinchang; Yang, Zhouwang: Alternating direction method of multipliers for solving dictionary learning models (2015)
  2. Zhang, Benxin; Zhu, Zhibin: A modified quasi-Newton diagonal update algorithm for total variation denoising problems and nonlinear monotone equations with applications in compressive sensing. (2015)
  3. Chen, K.; Piccolomini, E.Loli; Zama, F.: An automatic regularization parameter selection algorithm in the total variation model for image deblurring (2014)
  4. Ayvaci, Alper; Raptis, Michalis; Soatto, Stefano: Sparse occlusion detection with optical flow (2012)
  5. Bonettini, Silvia; Ruggiero, Valeria: On the convergence of primal-dual hybrid gradient algorithms for total variation image restoration (2012)
  6. Chung, Julianne; Chung, Matthias; O’leary, Dianne P.: Optimal filters from calibration data for image deconvolution with data acquisition error (2012)
  7. Jensen, T.L.; Jørgensen, J.H.; Hansen, P.C.; Jensen, S.H.: Implementation of an optimal first-order method for strongly convex total variation regularization (2012)
  8. Becker, Stephen; Bobin, Jér^ome; Candès, Emmanuel J.: NESTA: A fast and accurate first-order method for sparse recovery (2011)
  9. Bonettini, S.; Ruggiero, V.: An alternating extragradient method for total variation-based image restoration from Poisson data (2011)
  10. Dahl, Joachim; Hansen, Per Christian; Jensen, Søren Holdt; Jensen, Tobias Lindstrøm: Algorithms and software for total variation image reconstruction via first-order methods (2010)
  11. Hansen, Per Christian: Discrete inverse problems. Insight and algorithms. (2010)
  12. Yu, Gaohang; Qi, Liqun; Dai, Yuhong: On nonmonotone Chambolle gradient projection algorithms for total variation image restoration (2009) ioport