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Most published authors (scientific articles only) of the journal
scientific articles published in peer review journal, serial, conference publications, indexed in international bibliographical databases and/or having DOI index
|
1. |
V. V. Kotlyar |
123 |
2. |
A. A. Kovalev |
73 |
3. |
S. N. Khonina |
68 |
4. |
L. L. Doskolovich |
52 |
5. |
A. G. Nalimov |
49 |
6. |
S. S. Stafeev |
48 |
7. |
N. L. Kazanskii |
45 |
8. |
V. V. Myasnikov |
29 |
9. |
S. I. Kharitonov |
28 |
10. |
R. V. Skidanov |
27 |
11. |
A. P. Porfirev |
26 |
12. |
S. V. Karpeev |
24 |
13. |
D. A. Bykov |
23 |
14. |
E. S. Kozlova |
22 |
15. |
S. G. Volotovsky |
21 |
16. |
A. V. Kupriyanov |
20 |
17. |
V. V. Podlipnov |
20 |
18. |
Yu. V. Vizilter |
17 |
19. |
A. V. Volyar |
17 |
20. |
S. A. Degtyarev |
16 |
21. |
M. A. Moiseev |
16 |
22. |
A. V. Ustinov |
16 |
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40 most published authors of the journal |
|
Most cited authors of the journal |
1. |
N. L. Kazanskii |
572 |
2. |
S. N. Khonina |
512 |
3. |
V. V. Kotlyar |
464 |
4. |
S. I. Kharitonov |
256 |
5. |
A. G. Nalimov |
254 |
6. |
L. L. Doskolovich |
246 |
7. |
A. A. Kovalev |
238 |
8. |
S. S. Stafeev |
211 |
9. |
A. V. Kupriyanov |
196 |
10. |
V. V. Podlipnov |
188 |
11. |
R. V. Skidanov |
173 |
12. |
A. V. Nikonorov |
163 |
13. |
V. V. Myasnikov |
155 |
14. |
A. V. Volyar |
154 |
15. |
S. V. Karpeev |
145 |
16. |
E. S. Kozlova |
143 |
17. |
S. G. Volotovsky |
142 |
18. |
Ya. E. Akimova |
138 |
19. |
N. A. Ivliev |
136 |
20. |
S. P. Murzin |
135 |
|
40 most cited authors of the journal |
|
Most cited articles of the journal |
1. |
MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream V. V. Arlazarov, K. B. Bulatov, T. S. Chernov, V. L. Arlazarov Computer Optics, 2019, 43:5, 818–824 |
68 |
2. |
Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks N. A. Andriyanov, V. E. Dementiev, A. G. Tashlinskiy Computer Optics, 2022, 46:1, 139–159 |
61 |
3. |
Image restoration in diffractive optical systems using deep learning and deconvolution A. V. Nikonorov, M. V. Petrov, S. A. Bibikov, V. V. Kutikova, A. A. Morozov, N. L. Kazanskiy Computer Optics, 2017, 41:6, 875–887 |
61 |
4. |
Injectional multilens molding parameters optimization N. L. Kazanskiy, I. S. Stepanenko, A. I. Khaimovich, S. V. Kravchenko, E. V. Byzov, M. A. Moiseev Computer Optics, 2016, 40:2, 203–214 |
56 |
5. |
Addressed fiber Bragg structures in quasi-distributed microwave-photonic sensor systems O. G. Morozov, A. Zh. Sakhabutdinov Computer Optics, 2019, 43:4, 535–543 |
50 |
6. |
On the use of a multi-raster input of one-dimensional signals in two-dimensional optical correlators M. S. Kuzmin, V. V. Davydov, S. A. Rogov Computer Optics, 2019, 43:3, 391–396 |
49 |
7. |
A vector optical vortex generated and focused using a metalens V. V. Kotlyar, A. G. Nalimov Computer Optics, 2017, 41:5, 645–654 |
47 |
8. |
Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches E. V. Myasnikov Computer Optics, 2017, 41:4, 564–572 |
46 |
9. |
Russian traffic sign images dataset V. I. Shakhuro, A. S. Konushin Computer Optics, 2016, 40:2, 294–300 |
46 |
10. |
Achievements in the development of plasmonic waveguide sensors for measuring the refractive index N. L. Kazanskiy, M. Butt, S. A. Degtyarev, S. N. Khonina Computer Optics, 2020, 44:3, 295–318 |
39 |
11. |
Optical elements based on silicon photonics M. Butt, S. N. Khonina, N. L. Kazanskiy Computer Optics, 2019, 43:6, 1079–1083 |
39 |
12. |
Method for forecasting changes in time series parameters in digital information management systems Yu. A. Kropotov, A. Yu. Proskuryakov, A. A. Belov Computer Optics, 2018, 42:6, 1093–1100 |
39 |
13. |
Vegetation type recognition in hyperspectral images using a conjugacy indicator S. A. Bibikov, N. L. Kazanskiy, V. A. Fursov Computer Optics, 2018, 42:5, 846–854 |
37 |
14. |
Characteristics of sharp focusing of vortex Laguerre-Gaussian beams D. A. Savelyev, S. N. Khonina Computer Optics, 2015, 39:5, 654–662 |
37 |
15. |
Investigation of algorithms for coagulate arrangement in fundus images A. S. Shirokanev, D. V. Kirsh, N. Yu. Ilyasova, A. V. Kupriyanov Computer Optics, 2018, 42:4, 712–721 |
36 |
16. |
Modeling the performance of a spaceborne hyperspectrometer based on the Offner scheme N. L. Kazanskiy, S. I. Kharitonov, L. L. Doskolovich, A. V. Pavelev Computer Optics, 2015, 39:1, 70–76 |
36 |
17. |
Reconstruction of anatomical structures using statistical shape modeling N. A. Smelkina, R. N. Kosarev, A. V. Nikonorov, I. M. Bairikov, K. N. Ryabov, E. V. Avdeev, N. L. Kazanskiy Computer Optics, 2017, 41:6, 897–904 |
35 |
18. |
Using coupled photonic crystal cavities for increasing of sensor sensitivity A. V. Egorov, N. L. Kazanskiy, P. G. Serafimovich Computer Optics, 2015, 39:2, 158–162 |
31 |
19. |
U-Net-bin: hacking the document image binarization contest P. V. Bezmaternykh, D. A. Ilin, D. P. Nikolaev Computer Optics, 2019, 43:5, 825–832 |
30 |
20. |
An adaptive image inpainting method based on the modified Mumford-Shah model and multiscale parameter estimation D. N. Thanh, V. Surya Prasath, N. Son, L. M. Hieu Computer Optics, 2019, 43:2, 251–257 |
29 |
|
40 most cited articles of the journal |
|
Total publications: |
1232 |
Scientific articles: |
1219 |
Authors: |
1623 |
Citations: |
6265 |
Cited articles: |
903 |
|
Impact Factor Web of Science |
|
for 2023:
1.100 |
|
Scopus Metrics |
|
2023 |
CiteScore |
4.200 |
|
2023 |
SNIP |
0.575 |
|
2023 |
SJR |
0.251 |
|
2022 |
SJR |
0.321 |
|
2021 |
SJR |
0.508 |
|
2020 |
SJR |
0.491 |
|
2019 |
SJR |
0.586 |
|
2018 |
CiteScore |
2.370 |
|
2018 |
SJR |
0.535 |
|
2017 |
CiteScore |
1.790 |
|
2017 |
SNIP |
1.681 |
|
2017 |
SJR |
0.457 |
|
2016 |
CiteScore |
1.610 |
|
2016 |
SNIP |
1.495 |
|
2016 |
SJR |
0.348 |
|
2015 |
CiteScore |
1.220 |
|
2015 |
SNIP |
1.261 |
|
2015 |
IPP |
1.185 |
|
2015 |
SJR |
0.445 |
|
2014 |
CiteScore |
0.730 |
|
2014 |
SNIP |
0.846 |
|
2014 |
IPP |
0.656 |
|
2014 |
SJR |
0.285 |
|
2013 |
SNIP |
0.397 |
|
2013 |
IPP |
0.341 |
|
2013 |
SJR |
0.198 |
|