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Deep learning-based video stream reconstruction in mass-production diffractive optical systems
V. Evdokimova 1,2, M. Petrov 1,2, M. Klyueva 1,2, E. Zybin 3, V. Kosianchuk 3, I. Mishchenko 3, V. Novikov 3, N. Selvesiuk 3, E. Ershov 4, N. Ivliev 1,2, R. Skidanov 1,2, N. Kazanskiy 1,2, A. Nikonorov 1,2

Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34,
IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151,
Federal State Unitary Enterprise State Research Institute of Aviation Systems, 125319, Russia, Moscow, Viktorenko, 7,
Institute for Information Transmission Problems, RAS, 127051, Moscow, Russia, Bolshoy Karetny per. 19, build 1

 PDF, 4967 kB

DOI: 10.18287/2412-6179-CO-834

Pages: 130-141.

Full text of article: Russian language.

Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision systems. However, additional causes of quality loss have been encountered in the development of such systems. This study investigates the influence on the reconstructed image quality of such factors as limitations of mass production technology for diffractive optics, lossy video stream compression artifacts, and specificities of a neural network approach to image reconstruction. The paper offers an end-to-end deep learning-based image reconstruction framework to compensate for the additional factors of quality losing. It provides the image reconstruction quality sufficient for applied vision systems.

diffractive optics, diffractive lenses, deep learning-based reconstruction, image processing.

Evdokimova VV, Petrov MV, Klyueva MA, Zybin EY, Kosianchuk VV, Mishchenko IB, Novikov VM, Selvesiuk NI, Ershov EI, Ivliev NA, Skidanov RV, Kazanskiy NL, Nikonorov AV. Deep learning-based video stream reconstruction in mass production diffractive optical systems. Computer Optics 2021; 45(1): 130-141. DOI: 10.18287/2412-6179-CO-834.

The theoretical part and neural network models were developed with the support from the Russian Science Foundation under RSF grant 20-69-47110. The experimental part was executed with the support from the Russian Foundation for Basic Research under RFBR grant 18-07-01390-А and under the government project of the IPSI RAS – a branch of the Federal Scientific-Research Center "Crystallography and Photonics" of the RAS (agreement 007-ГЗ/Ч3363/2б).


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