DocumentCode
3539965
Title
Diffracted image restoration: A machine learning approach
Author
Koudelka, V. ; del Rio Bocio, C. ; Raida, Zbynek
Author_Institution
Dept. of Radio-Electron., Brno Univ. of Technol., Brno, Czech Republic
fYear
2013
fDate
9-13 Sept. 2013
Firstpage
931
Lastpage
934
Abstract
Image restoration issues are closely connected with imaging systems, where image resolution is limited by diffraction phenomenon. The presented work is motivated by the super acuity of the Human vision, where the restoration step is implemented by some kind of parallel processor unit - neural network. The de-convolution process is formulated as a machine learning problem and the inverse operator is interpreted as a connectionist model.
Keywords
deconvolution; diffraction; image resolution; image restoration; learning (artificial intelligence); mathematical operators; neural nets; parallel processing; connectionist model; deconvolution process; diffracted image restoration; diffraction phenomenon; human vision; image resolution; imaging systems; inverse operator; machine learning approach; neural network; parallel processor unit; super acuity; Diffraction; Image restoration; Imaging; Noise; Sensors; Stability analysis; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Electromagnetics in Advanced Applications (ICEAA), 2013 International Conference on
Conference_Location
Torino
Print_ISBN
978-1-4673-5705-0
Type
conf
DOI
10.1109/ICEAA.2013.6632375
Filename
6632375
Link To Document