IMAGE COMPLETION WITH LOW-RANK MODEL APPROXIMATION METHODS

Tomasz Sadowski

tomasz.sadowski@pwr.edu.pl
Politechnika Wrocławska, Wydział Elektroniki (Poland)

Rafał Zdunek


Politechnika Wrocławska, Wydział Elektroniki (Poland)

Abstract

The paper is concerned with the task of reconstructing missing pixels in images perturbed with impulse noise in a transmission channel. Such a task can be formulated in the context of image interpolation on an irregular grid or by approximating an incomplete image by low-rank factor decomposition models. We compared four algorithms that are based on the low-rank decomposition model: SVT, SmNMF-MC , FCSA-TC and SPC-QV. The numerical experiments are carried out for various cases of incomplete images, obtained by removing random pixels or regular grid lines from test images. The best performance is obtained if nonnegativity and smoothing constraints are imposed onto the estimated low-rank factors.


Keywords:

image completion, low-rank approximation, nonnegative matrix factorization, tensor decomposition, matrix completion

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Published
2017-12-21

Cited by

Sadowski, T., & Zdunek, R. (2017). IMAGE COMPLETION WITH LOW-RANK MODEL APPROXIMATION METHODS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 7(4), 44–48. https://doi.org/10.5604/01.3001.0010.7259

Authors

Tomasz Sadowski 
tomasz.sadowski@pwr.edu.pl
Politechnika Wrocławska, Wydział Elektroniki Poland

Authors

Rafał Zdunek 

Politechnika Wrocławska, Wydział Elektroniki Poland

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