IMAGE COMPLETION WITH LOW-RANK MODEL APPROXIMATION METHODS
Tomasz Sadowski
tomasz.sadowski@pwr.edu.plPolitechnika 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 completionReferences
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Authors
Tomasz Sadowskitomasz.sadowski@pwr.edu.pl
Politechnika Wrocławska, Wydział Elektroniki Poland
Authors
Rafał ZdunekPolitechnika Wrocławska, Wydział Elektroniki Poland
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