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Image Completion with Smooth Nonnegative Matrix Factorization

  • Tomasz SadowskiEmail author
  • Rafał Zdunek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

Nonnegative matrix factorization is an unsupervised learning method for part-based feature extraction and dimensionality reduction of nonnegative data with a variety of models, algorithms, structures, and applications. Smooth nonnegative matrix factorization assumes the estimated latent factors are locally smooth, and the smoothness is enforced by the underlying model or the algorithm. In this study, we extended one of the algorithms for this kind of factorization to an image completion problem. It is the B-splines ADMM-NMF (Nonnegative Matrix Factorization with Alternating Direction Method of Multipliers) that enforces smooth feature vectors by assuming they are represented by a linear combination of smooth basis functions, i.e. B-splines. The numerical experiments performed on several incomplete images show that the proposed method outperforms the other algorithms in terms of the quality of recovered images.

Notes

Acknowledgment

This work was partially supported by the grant 2015/17/B/ST6/01865 funded by National Science Center (NCN) in Poland. Calculations were performed at the Wroclaw Center for Networking and Supercomputing under grant no. 127.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of ElectronicsWroclaw University of Science and TechnologyWroclawPoland

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