Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Yuriy Yuzifovich"'
Autor:
Viktoria V. Evdokimova, Vladimir V. Podlipnov, Nikolay A. Ivliev, Maxim V. Petrov, Sofia V. Ganchevskaya, Vladimir A. Fursov, Yuriy Yuzifovich, Sergey O. Stepanenko, Nikolay L. Kazanskiy, Artem V. Nikonorov, Roman V. Skidanov
Publikováno v:
Sensors, Vol 23, Iss 1, p 415 (2022)
In this paper, we present a hybrid refractive-diffractive lens that, when paired with a deep neural network-based image reconstruction, produces high-quality, real-world images with minimal artifacts, reaching a PSNR of 28 dB on the test set. Our dif
Externí odkaz:
https://doaj.org/article/0fa8d4c9663c4ccc87344543914a95f3
Autor:
Albert Gareev, Vladimir Protsenko, Dmitriy Stadnik, Pavel Greshniakov, Yuriy Yuzifovich, Evgeniy Minaev, Asgat Gimadiev, Artem Nikonorov
Publikováno v:
Sensors, Vol 21, Iss 13, p 4410 (2021)
This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augment
Externí odkaz:
https://doaj.org/article/66ef6e6b89e94949a05d6bf7b1945da3
Autor:
Maksim Petrov, Roman V. Skidanov, Viktoriya V. Kutikova, Andrey Morozov, Artem V. Nikonorov, Pavel Yakimov, Nikolay L. Kazanskiy, Sergei Bibikov, Yuriy Yuzifovich
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11:3338-3348
In this paper, we describe our advances in manufacturing a 256-layer 7- μ m thick harmonic lens with 150 and 300 mm focal distances combined with color correction, deconvolution, and a feedforwarding deep learning neural network capable of producing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d9e260d23f88e45d05a53c351d54b6e
https://doi.org/10.1109/jstars.2018.2856538
https://doi.org/10.1109/jstars.2018.2856538
Autor:
Artem V. Nikonorov, Dmitriy Stadnik, Albert Gareev, Pavel Greshniakov, Yuriy Yuzifovich, Evgeniy Minaev, Gimadiev Asgat G, Vladimir Protsenko
Publikováno v:
Sensors
Volume 21
Issue 13
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 4410, p 4410 (2021)
Volume 21
Issue 13
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 4410, p 4410 (2021)
This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augment
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f72aabce946c358c7d675c979a7f079e
https://doi.org/10.3390/s21134410
https://doi.org/10.3390/s21134410
Publikováno v:
Procedia Engineering. 201:73-82
Diffractive optical elements (DOE) have significant advantage over refractive optics in linear dimensions and weight. However, the quality of images produced by a diffractive optics-based system suffers from strong distortions. In previous papers, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a3bc31f6ab07c5d59feedb194bcad696
https://doi.org/10.1016/j.proeng.2017.09.665
https://doi.org/10.1016/j.proeng.2017.09.665
Autor:
Artem V. Nikonorov, S. A. Bibikov, Nikolay L. Kazanskiy, Roman V. Skidanov, Yuriy Yuzifovich, Maksim Petrov, Pavel Yakimov, Viktoria Evdokimova
Publikováno v:
ICCV Workshops
The pressure to reduce weight and improve image quality of the imaging devices continues to push research in the area of flat optics with computational image reconstruction. This paper presents a new end-to-end framework applying two convolutional ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e59fbfd1e24a9a9766a8a43d2641cdcf
https://doi.org/10.1109/iccvw.2019.00491
https://doi.org/10.1109/iccvw.2019.00491
Publikováno v:
Pattern Recognition Letters. 83:178-187
This paper presents a novel identification-based image correction method using a bi-illuminant dichromatic reflection model. Image patches with uniform properties over distorted and distortion-free images or image parts are used as a prior knowledge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1da71522d3496053668cd528c9edd32a
https://doi.org/10.1016/j.patrec.2016.06.027
https://doi.org/10.1016/j.patrec.2016.06.027
Autor:
S. A. Bibikov, Nikolay L. Kazanskiy, Vladimir Fursov, Yuriy Yuzifovich, Pavel Yakimov, Roman V. Skidanov, Maksim Petrov, Artem V. Nikonorov
Publikováno v:
ICPR
With suggested computational post-processing workflow for correcting optical distortions, the Fresnel lens can finally be used in lightweight and inexpensive computer vision sensors. Common methods for image enhancement do not comprehensively address
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0544bc50396acb1a2497b4b3702c592a
https://doi.org/10.1109/icpr.2016.7899729
https://doi.org/10.1109/icpr.2016.7899729
Autor:
Evgeny Prilepin, Yuriy Yuzifovich, Alexandr Kolsanov, Artem V. Nikonorov, Sergey Chaplygin, Maksim Petrov, K. Bychenkov, Pavel M. Zelter
Publikováno v:
E-Business and Telecommunications ISBN: 9783319302218
ICETE (Selected Papers)
ICETE (Selected Papers)
This paper describes a comprehensive multi-step algorithm for vascular structure segmentation in CT scan data, from raw slice images to a 3D object, with an emphasis on improving segmentation quality and assessing computational complexity. To estimat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8587d0c37d3aea968417f0ac593c849a
https://doi.org/10.1007/978-3-319-30222-5_23
https://doi.org/10.1007/978-3-319-30222-5_23
Autor:
Maksim Petrov, S. A. Bibikov, Yuriy Yuzifovich, Roman V. Skidanov, Artem V. Nikonorov, Vladimir Fursov
Publikováno v:
CVPR Workshops
This paper describes a unified approach to correct optical distortions in images formed by a Fresnel lens with computational post-processing that opens up new opportunities to use Fresnel lenses in lightweight and inexpensive computer vision devices.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14386c5bea31a77e425ad54e58578af1
https://doi.org/10.1109/cvprw.2015.7301373
https://doi.org/10.1109/cvprw.2015.7301373