Получение цветных изображений системой на основе трех дифракционных линз
С. О. Степаненко, В. В. Евдокимова, М. В. Петров, Р. В. Скиданов, А. В. Никоноров

Список литературы
1.	Chen MK, Liu X, Sun Y, Tsai DP, “Artificial intelligence in meta-optics”, Chem Rev, 122:19 (2022), 15356–15413
2.	Genevet P, Capasso F, Aieta F, Khorasaninejad M, Devlin R, “Recent advances in planar optics: from plasmonic to dielectric metasurfaces”, Optica, 4:1 (2017), 139–152
3.	Banerji S, et al., “Imaging with flat optics: metalenses or diffractive lenses?”, Optica, 6:6 (2019), 805–810
4.	Nikonorov A, Evdokimova V, Petrov M, Yakimov P, Bibikov S, Yuzifovich Y, Skidanov R, Kazanskiy N, “Deep learning-based imaging using single-lens and multi-aperture diffractive optical systems”, 2019 IEEE/CVF Int Conf on Computer Vision Workshop (ICCVW), 2019, 3969–3977
5.	Kazanskii NL, Khonina SN, Skidanov RV, Morozov AA, Kharitonov SI, Volotovskiy SG, “Formation of images using multilevel diffractive lens”, Computer Optics, 38:3 (2014), 425–434
6.	Nikonorov A, Skidanov R, Fursov V, Petrov M, Bibikov S, Yuzifovich Y, “Fresnel lens imaging with post-capture image processing”, 2015 IEEE Conf on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015, 33–41
7.	Nikonorov A, Petrov M, Bibikov S, Yakimov P, Kutikova V, Yuzifovich Y, Morozov A, Skidanov R, Kazanskiy N, “Toward ultralightweight remote sensing with harmonic lenses and convolutional neural networks”, IEEE J Sel Top Appl Earth Obs Remote Sens, 11:9 (2018), 3338–3348
8.	Nikonorov AV, Skidanov RV, Kutikova VV, Petrov MV, Alekseev AP, Bibikov SA, Kazanskiy NL, “Towards multi-aperture imaging using diffractive lens”, Proc SPIE, 11146 (2019), 111460Y
9.	Nikonorov A, Petrov M, Bibikov S, Yuzifovich Y, Yakimov P, Kazanskiy N, Skidanov R, Fursov V, “Comparative evaluation of deblurring techniques for fresnel lens computational imaging”, 2016 23rd Int Conf on Pattern Recognition (ICPR), 2016, 775-780
10.	Peng Y, Fu Q, Amata H, Su Sh, Heide F, Heidrich W, “Computational imaging using lightweight diffractive-refractive optics”, Opt Express, 23:24 (2015), 31393–31407
11.	Skidanov R, Strelkov Y, Volotovsky S, Blank V, Ganchevskaya S, Podlipnov V, Ivliev N, Kazanskiy N, “Compact imaging systems based on annular harmonic lenses”, Sensors, 20:14 (2020), 3914
12.	Sales TRM, Morris GM, “Diffractive-refractive behavior of kinoform lenses”, Appl Opt, 36:1 (1997), 253–257
13.	Mohammad N, Meem M, Shen B, Wang P, Menon R, “Broadband imaging with one planar diffractive lens”, Sci Rep, 8 (2018), 2799
14.	Kim G, Domínguez-Caballero JA, Menon R, “Design and analysis of multi-wavelength difractive optics”, Opt Express, 20:3 (2012), 2814–2823
15.	Blank V, Skidanov R, Doskolovich L, Kazanskiy N, “Spectral diffractive lenses for measuring a modified red edge simple ratio index and a water band index”, Sensors, 21:22 (2021), 7694
16.	Blank VA, Skidanov RV, Doskolovich LL, “Investigation of a spectral lens for the formation of a normalized difference vegetation index NDVI0.705”, J Opt Technol, 89:3 (2022), 137–141
17.	Doskolovich LL, Golub MA, Kazanskiy NL, Khramov AG, Pavelyev VS, Seraphimovich PG, Soifer VA, Volotovskiy SG, “Software on diffractive optics and computer generated holograms”, Proc. SPIE, 2363 (1995), 278–284
18.	Kazanskiy NL, “Modeling diffractive optics elements and devices”, Proc SPIE, 10774 (2018), 107740O
19.	Kazanskiy NL, “Research & Education Center of Diffractive Optics”, Proc SPIE, 8410 (2012), 84100R
20.	Kazanskiy NL, Skidanov RV, “Technological line for creation and research of diffractive optical elements”, Proc SPIE, 11146 (2019), 111460W
21.	Zhang Y, Li Ku, Li K, Wang L, Zhong B, Fu Y, “Image super-resolution using very deep residual channel attention networks”, Computer Vision – ECCV 2018, eds. Ferrari V, Hebert M, Sminchisescu C, Weiss Y, Springer Nature Switzerland AG, Cham, 2018, 294–310
22.	Kim J, Lee JK, Lee KM, “Accurate image super-resolution using very deep convolutional networks”, 2016 IEEE Conf on Computer Vision and Pattern Recognition (CVPR), 2016, 1646–1654
23.	Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y, “Residual dense network for image super-resolution”, 2018 IEEE/CVF Conf on Computer Vision and Pattern Recognition, 2018, 2472–2481
24.	Wang L, Wang Y, Dong X, Xu Q, Yang J, An W, Guo Y, “Unsupervised degradation representation learning for blind superresolution”, 2021 IEEE/CVF Conf on Computer Vision and Pattern Recognition (CVPR), 2021, 10576–10585
25.	Wang Z, Chen J, Hoi SCH, “Deep learning for image super-resolution: A survey”, IEEE Trans Pattern Anal Mach Intell, 43:10 (2020), 3365–3387
26.	Dong C, Loy CC, He K, Tang X, “Learning a deep convolutional network for image super-resolution”, Computer Vision – ECCV 2014, eds. Fleet D, Pajdla T, Schiele B, Tuytelaars T, Springer International Publishing Switzerland, Cham, 2014, 184–199
27.	Zhang K, Zuo W, Zhang L, “Learning a single convolutional super-resolution network for multiple degradations”, 2018 IEEE/CVF Conf on Computer Vision and Pattern Recognition, 2018, 3262–3271
28.	Wang X, Xie L, Dong C, Shan Y, “Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data”, IEEE/CVF Int Conf on Computer Vision Workshops (ICCVW), 2021, 1904–1914
29.	Soh JW, Cho S, Cho NI, “Meta-transfer learning for zero-shot super-resolution”, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, 3513–3522
30.	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, 45:1 (2021), 130–141
31.	Ivliev N, Evdokimova V, Podlipnov V, Petrov M, Ganchevskaya S, Tkachenko I, Abrameshin D, Yuzifovich Y, Nikonorov A, Skidanov R, Kazanskiy N, Soifer V, “First Earth-imaging CubeSat with harmonic diffractive lens”, Remote Sens, 14:9 (2022), 2230
32.	Ronneberger O, “U-Net: Convolutional networks for biomedical image segmentation”, Medical image computing and computerassisted intervention – MICCAI, eds. Navab N, Hornegger J, Wells WM, Frangi AF, Springer, New York, Dordrecht, London, 2015, 234–241
33.	Dun X, Ikoma H, Wetzstein G, Wang Z, Cheng X, Peng Y, “Learned rotationally symmetric diffractive achromat for full-spectrum computational imaging”, Optica, 7:8 (2020), 913–922
34.	Peng Y, Sun Q, Dun X, Wetzstein G, Heidrich W, Heide F, “Learned large field-of-view imaging with thin-plate optics”, ACM Trans Graph, 38:6 (2019), 219
35.	Foi A, Trimeche M, Katkovnik V, Egiazarian K, “Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data”, IEEE Trans Image Process, 17:10 (2008), 1737–1754
36.	Guo S, Yan Z, Zhang K, Zuo W, Zhang L, “Toward convolutional blind denoising of real photographs”, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, 1712–1722
37.	Do more with less data, 2023 https://albumentations.ai/
38.	Ying Z, Li G, Ren Y, Wang R, Wang W, “A new low-light image enhancement algorithm using camera response model”, IEEE Int Conf on Computer Vision Workshops (ICCVW), 2017, 3015–3022
39.	Karpeev SV, Alferov SV, Khonina SN, Kudryashov SI, “Study of the broadband radiation intensity distribution formed by diffractive optical elements”, Computer Optics, 38:4 (2014), 689–694