Персона: Черёмхин, Павел Аркадьевич
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Институт лазерных и плазменных технологий
Стратегическая цель Института ЛаПлаз – стать ведущей научной школой и ядром развития инноваций по лазерным, плазменным, радиационным и ускорительным технологиям, с уникальными образовательными программами, востребованными на российском и мировом рынке образовательных услуг.
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Павел Аркадьевич
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- ПубликацияТолько метаданныеInterpolation-Filtering Method for Image Improvement in Digital Holography(2024) Kozlov,A.V.; Cheremkhin,P.A.; Svistunov,A.S.; Rodin,V.G.; Starikov,R.S.; Evtikhiev,N.N.; Козлов, Александр Валерьевич; Черёмхин, Павел Аркадьевич; Свистунов, Андрей Сергеевич; Родин, Владислав Геннадьевич; Стариков, Ростислав Сергеевич; Евтихиев, Николай НиколаевичDigital holography is actively used for the characterization of objects and 3D-scenes, tracking changes in medium parameters, 3D shape reconstruction, detection of micro-object positions, etc. To obtain high-quality images of objects, it is often necessary to register a set of holograms or to select a noise suppression method for specific experimental conditions. In this paper, we propose a method to improve filtering in digital holography. The method requires a single hologram only. It utilizes interpolation upscaling of the reconstructed image size, filtering (e.g., median, BM3D, or NLM), and interpolation to the original image size. The method is validated on computer-generated and experimentally registered digital holograms. Interpolation methods coefficients and filter parameters were analyzed. The quality is improved in comparison with digital image filtering up to 1.4 times in speckle contrast on the registered holograms and up to 17% and 29% in SSIM and NSTD values on the computer-generated holograms. The proposed method is convenient in practice since its realization requires small changes of standard filters, improving the quality of the reconstructed image.
- ПубликацияТолько метаданныеDMD-based optical pattern recognition using holograms generated with the Hartley transform(2023) Cheremkhin, P. A.; Krasnov, V. V.; Rodin, V. G.; Starikov, R. S.; Черёмхин, Павел Аркадьевич; Родин, Владислав Геннадьевич; Стариков, Ростислав Сергеевич
- ПубликацияОткрытый доступWhat Binarization Method Is the Best for Amplitude Inline Fresnel Holograms Synthesized for Divergent Beams Using the Direct Search with Random Trajectory Technique?(2023) Ovchinnikov, A. S.; Krasnov, V. V.; Cheremkhin, P. A.; Rodin, V. G.; Savchenkova, E. A.; Starikov, R. S.; Evtikhiev, N. N.; Овчинников, Андрей Сергеевич; Черёмхин, Павел Аркадьевич; Родин, Владислав Геннадьевич; Савченкова, Екатерина Алексеевна; Стариков, Ростислав Сергеевич; Евтихиев, Николай НиколаевичFast reconstruction of holographic and diffractive optical elements (DOE) can be implemented by binary digital micromirror devices (DMD). Since micromirrors of the DMD have two positions, the synthesized DOEs must be binary. This work studies the possibility of improving the method of synthesis of amplitude binary inline Fresnel holograms in divergent beams. The method consists of the modified Gerchberg–Saxton algorithm, Otsu binarization and direct search with random trajectory technique. To achieve a better quality of reconstruction, various binarization methods were compared. We performed numerical and optical experiments using the DMD. Holograms of halftone image with size up to 1024 Г— 1024 pixels were synthesized. It was determined that local and several global threshold methods provide the best quality. Compared to the Otsu binarization used in the original method of the synthesis, the reconstruction quality (MSE and SSIM values) is improved by 46% and the diffraction efficiency is increased by 27%.
- ПубликацияОткрытый доступHoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network(2023) Svistunov, A. S.; Rymov, D. A.; Starikov, R. S.; Cheremkhin, P. A.; Свистунов, Андрей Сергеевич; Рымов, Дмитрий Андреевич; Стариков, Ростислав Сергеевич; Черёмхин, Павел АркадьевичReconstruction of 3D scenes from digital holograms is an important task in different areas of science, such as biology, medicine, ecology, etc. A lot of parameters, such as the object’s shape, number, position, rate and density, can be extracted. However, reconstruction of off-axis and especially inline holograms can be challenging due to the presence of optical noise, zero-order image and twin image. We have used a deep-multibranch neural network model, which we call HoloForkNet, to reconstruct different 2D sections of a 3D scene from a single inline hologram. This paper describes the proposed method and analyzes its performance for different types of objects. Both computer-generated and optically registered digital holograms with resolutions up to 2048 Г— 2048 pixels were reconstructed. High-quality image reconstruction for scenes consisting of up to eight planes was achieved. The average structural similarity index (SSIM) for 3D test scenes with eight object planes was 0.94. The HoloForkNet can be used to reconstruct 3D scenes consisting of micro- and macro-objects.