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Computer Optics, 2017, Volume 41, Issue 6, Pages 875–887 (Mi co461)  

IMAGE PROCESSING, PATTERN RECOGNITION

Image restoration in diffractive optical systems using deep learning and deconvolution

A. V. Nikonorovab, M. V. Petrova, S. A. Bibikova, V. V. Kutikovaa, A. A. Morozovab, N. L. Kazanskiyba

a Samara National Research University, Samara, Russia
b Image Processing Systems Institute of the RAS - Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia

Abstract: In recent years, several pioneering works were dedicated to imaging systems based on simple diffractive structures like Fresnel lenses or phase zone plates. Such systems are much lighter and cheaper than classical refractive optical systems. However, the quality of images obtained by diffractive optics suffers from stronger distortions of various types. In this paper, we show that a combination of the high-precision lens design with post-capture computational reconstruction allows one to attain a much higher image quality. The proposed reconstruction procedure uses a sequence of color correction, deconvolution, and a feedforward deep learning neural network. An improvement both in lens manufacturing and in image processing may contribute to the emergence of ultra-lightweight imaging systems varying from cameras for nano- and picosatellites to surveillance systems.

Keywords: harmonic lens, remote sensing, deconvolution, deep learning, PSF estimation, color correction.

Funding Agency Grant Number
Ministry of Education and Science of the Russian Federation -2531.2017.9
Russian Foundation for Basic Research 16-47-630721 _
16-29-09528 _
17-29-03112 _
The work was partially funded by the Russian Federation Ministry of Education and Science (Presidential grant MD-2531.2017.9) and the Russian Foundation for Basic Research (RFBR grants 16-47-630721 r_a, 16-29-09528 ofi_m and 17-29-03112 ofi_m).


DOI: https://doi.org/10.18287/2412-6179-2017-41-6-875-887

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Full text: http://www.computeroptics.smr.ru/.../410613.html
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Received: 18.10.2017
Accepted:22.11.2017

Citation: A. V. Nikonorov, M. V. Petrov, S. A. Bibikov, V. V. Kutikova, A. A. Morozov, N. L. Kazanskiy, “Image restoration in diffractive optical systems using deep learning and deconvolution”, Computer Optics, 41:6 (2017), 875–887

Citation in format AMSBIB
\Bibitem{NikPetBib17}
\by A.~V.~Nikonorov, M.~V.~Petrov, S.~A.~Bibikov, V.~V.~Kutikova, A.~A.~Morozov, N.~L.~Kazanskiy
\paper Image restoration in diffractive optical systems using deep learning and deconvolution
\jour Computer Optics
\yr 2017
\vol 41
\issue 6
\pages 875--887
\mathnet{http://mi.mathnet.ru/co461}
\crossref{https://doi.org/10.18287/2412-6179-2017-41-6-875-887}


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