REKONSTRUKCJA NIEKOMPLETNYCH OBRAZÓW ZA POMOCĄ METOD APROKSYMACJI MODELAMI NISKIEGO RZĘDU

Tomasz Sadowski; Rafał Zdunek

doi:10.5604/01.3001.0010.7259

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Published: Dec 21, 2017

DOI: https://doi.org/10.5604/01.3001.0010.7259

Issue Vol. 7 No. 4 (2017)

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LABORATORY STAND FOR SMALL WIND TURBINE SIMULATION
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DEVELOPMENT OF AN AUTOMATED DIAGNOSTICS AND CONTROL SYSTEM FOR BIOGAS COMBUSTION PROCESSES
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SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD
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THE USE OF PETRI NETS IN DECISION SUPPORT SYSTEMS BASED ON INTELLIGENT MULTIPLY SOURCE DATA ANALYSIS
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APPLICABILITY ANALYSIS OF REST AND SOAP WEB SERVICES
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DIRECT TORQUE CONTROL OF MULTI-PHASE INDUCTION MOTOR WITH FUZZY LOGIC SPEED CONTROLLER
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IMAGE COMPLETION WITH LOW-RANK MODEL APPROXIMATION METHODS
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44-48
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DOI

https://doi.org/10.5604/01.3001.0010.7259

Authors

Tomasz Sadowski

tomasz.sadowski@pwr.edu.pl

Politechnika Wrocławska, Wydział Elektroniki, Poland

Rafał Zdunek

rafal.zdunek@pwr.edu.pl

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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