Analysis of focusing light by a harmonic diffractive lens taking into account the refractive index dispersion

Хонина, С. Н.; Volotovsky, S.; Ustinov, Andrey V.; Kharitonov, S. I.

doi:10.18287/2412-6179-2017-41-3-338-347

Cited by 19 publications

(2 citation statements)

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“…Russia has over 200 • 10 6 Ha of agricultural land area [19] and requires a rapid increase in agriculture production; therefore, the development of relevant technologies is of particular importance [20]. Among the works of Russian scientists, both fundamental research on computer optics methods [21][22][23] and physical processes of image registration [24,25] and applied research aimed at the development of integrated satellite monitoring data processing systems [26,27] can be highlighted. The results of these studies make it possible to improve both the hardware characteristics of remote sensing systems, for example, by significantly facilitating the registration system [28], and to improve the quality of the monitoring data obtained [29,30].…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data

Pavlova¹,

Timofeev²,

Bocharov³

et al. 2021

Preprint

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This paper considered the issue of agricultural fields boundary recognition in satellite images. A novel algorithm based on the aggregated history of vegetation index data obtained via open satellite data, Sentinel-2, was proposed. The proposed algorithm included several basic steps, namely the detection of parcel regions on aggregated index data; the calculation of aggregated edge maps; the segmentation of parcel regions using the edges obtained; the computation of connected components and their contour extraction. In this paper, we showed that the use of aggregated vegetation index data and boundary maps allow for much more accurate agricultural field segmentation compared to the instant vegetation index approach. The quality of segmentation within regions of Russia and the Ukraine was estimated. The dataset that was used and Python implementation of the proposed algorithm were provided.

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Section: Related Workmentioning

confidence: 99%

Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data

Pavlova¹,

Timofeev²,

Bocharov³

et al. 2021

Preprint

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“…A significant number of publications were dedicated to the analysis of refractive-diffractive lenses [ 11 , 12 , 13 ]. A reflecting lens that reflects light inside the glass and has an annular aperture, with a diffractive element being the only element used to compensate for chromatic aberration, was analyzed in the paper [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Refractive-Diffractive Lens with Reduced Chromatic and Geometric Aberrations and Learned Image Reconstruction

Evdokimova

Подлипнов

Ivliev

et al. 2022

Sensors

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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 diffractive element compensates for the off-axis aberrations of a single refractive element and has reduced chromatic aberrations across the visible light spectrum. We also describe our training set augmentation and novel quality criteria called “false edge level” (FEL), which validates that the neural network produces visually appealing images without artifacts under a wide range of ISO and exposure settings. Our quality criteria (FEL) enabled us to include real scene images without a corresponding ground truth in the training process.

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Exploring Diffractive Optical Elements and Their Potential in Free Space Optics and imaging‐ A Comprehensive Review

Khonina,

Kazanskiy,

Butt

2024

Laser & Photonics Reviews

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Diffractive Optical Elements (DOEs) are indispensable tools across numerous technological domains due to their capacity to manipulate light with sophistication and versatility. Their compact dimensions, lightweight nature, and compatibility with diverse materials render DOEs as prime candidates for integration into various optical systems, spanning from consumer electronics to state‐of‐the‐art scientific devices. Thus, DOEs stand as essential catalysts in technological advancement, facilitating innovation and unlocking new applications across a broad spectrum of disciplines. In this comprehensive review paper, Numerous types of DOEs widely acknowledge for their efficacy in both free space optics and imaging applications are delved. Beyond mere enumeration, their practical applications, elucidating their transformative impact on these fields are carefully examined. Furthermore, the challenges encountered in their implementation are dissected, paving the way for insightful discussions on future trajectories and advancements.

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Analysis of focusing light by a harmonic diffractive lens taking into account the refractive index dispersion

Cited by 19 publications

References 1 publication

Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data

Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data

Hybrid Refractive-Diffractive Lens with Reduced Chromatic and Geometric Aberrations and Learned Image Reconstruction

Exploring Diffractive Optical Elements and Their Potential in Free Space Optics and imaging‐ A Comprehensive Review

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