Low-rank and nonlinear model approach to image inpainting

Low-rank and nonlinear model approach to image inpainting. Sasaki, R., Konishi, K., Takahashi, T., & Furukawa, T. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 336-340, Aug, 2017.

Paper doi abstract bibtex

This paper proposes a new algorithm for image inpainting algorithm based on the matrix rank minimization with nonlinear mapping function. Assuming that each intensity value of a nonlinear mapped image can be modeled by the autoregressive (AR) model, the image inpainting problem is formulated as a kind of the matrix rank minimization problem, and this paper modifies the iterative partial matrix shrinkage (IPMS) algorithm and provides an inpainting algorithm, which estimates a nonlinear mapping function and the missing pixels simultaneously. Numerical examples show that the proposed algorithm recovers missing pixels efficiently.

@InProceedings{8081224,
  author = {R. Sasaki and K. Konishi and T. Takahashi and T. Furukawa},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Low-rank and nonlinear model approach to image inpainting},
  year = {2017},
  pages = {336-340},
  abstract = {This paper proposes a new algorithm for image inpainting algorithm based on the matrix rank minimization with nonlinear mapping function. Assuming that each intensity value of a nonlinear mapped image can be modeled by the autoregressive (AR) model, the image inpainting problem is formulated as a kind of the matrix rank minimization problem, and this paper modifies the iterative partial matrix shrinkage (IPMS) algorithm and provides an inpainting algorithm, which estimates a nonlinear mapping function and the missing pixels simultaneously. Numerical examples show that the proposed algorithm recovers missing pixels efficiently.},
  keywords = {autoregressive processes;image reconstruction;image resolution;iterative methods;matrix algebra;minimisation;nonlinear model approach;image inpainting algorithm;nonlinear mapping function;intensity value;nonlinear mapped image;autoregressive model;image inpainting problem;matrix rank minimization problem;iterative partial matrix shrinkage algorithm;IPMS algorithm;low-rank appoach;missing pixels recovery;Signal processing algorithms;Minimization;Numerical models;Subspace constraints;Signal processing;Approximation algorithms;Europe;image inpainting;matrix rank minimization;AR modeling;matrix recovery;manifold learning},
  doi = {10.23919/EUSIPCO.2017.8081224},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347729.pdf},
}

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