Effects of atmospheric turbulence on the imaging performance of optical system

Al-Hamadani, Ali H.; Zainulabdeen, Faten Sh.; Karam, Ghada Sabah; Nasir, Eman Yousif; Al-Saedi, Abaas.

doi:10.1063/1.5039258

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The quality of the image becomes particularly important because it affects the accurate interpretation and perception of the image. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred to a certain extent due to the acquisition of the imaging system, resulting in the degradation of quality of the images [ 1 , 2 , 3 , 4 ]. Therefore, it makes it hard for human observers to discriminate the fine details of the images such as edges and other features.…”

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Zheng

2021

Sensors

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Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.

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Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Zheng

2021

Sensors

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ARIES ST Radar: The First Central Himalayan Wind Profiler

Bhattacharjee

Naja

Jaiswal

et al. 2022

J. Astron. Instrum.

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Recently, a 206.5[Formula: see text]MHz Stratosphere Troposphere (ST) Radar system was successfully installed and made operationalized at Aryabhatta Research Institute of Observational Sciences (ARIES) (29.4N, 79.2E, 1793[Formula: see text]m amsl), Nainital, India. It is the first such unique observational facility located in the central Himalayan region and will play an important role in understanding the meteorological conditions of the region that has a vital role in atmospheric studies in South Asia. The entire ST radar system is indigenously built and installed in a compact 30[Formula: see text][Formula: see text][Formula: see text]m two-storey building, making maximum use of the available space in the hilly terrain. A metal fence of 3.5–4[Formula: see text]m height was designed and installed along the perimeter of the array to attenuate the clutter returns from the nearby mountains with the shielding efficiency 22–25[Formula: see text]dB. Since its operation, the radar has obtained useful data of neutral atmosphere, precipitation, convection, and hailstorm events for scientific research. The technical details of different sub-systems, radar integration and calibration methodology are presented here. A dedicated off-line GUI based data processing tool has been developed and is being used for the data analysis. A comparison of wind components derived from ARIES ST Radar with collocated GPS-radiosonde observations indicates a good agreement with correlation coefficients for zonal (0.92), meridional (0.76), wind speed (0.86), and wind direction (0.7). The change in wind patterns is demonstrated up to a height of about 31[Formula: see text]km amsl and the tropopause was marked to be at 16–17[Formula: see text]km on 2020 June 20. A dramatic reversal of winds from westerly (below the tropopause) to easterly (above the tropopause) was also observed. ARIES ST Radar could capture the signature of the precipitation in addition to neutral air in the same Doppler spectrum and the height of the starting point of precipitation is identified to be [Formula: see text][Formula: see text]km. This ability to detect atmospheric scattering from both neutral wind (Bragg) and precipitation (Rayleigh) in the same spectrum makes the 200[Formula: see text]MHz band radar a unique instrument in the wind profiler application for atmospheric research.

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Graph Convolutional Network for Image Restoration: A Survey

Cheng,

Bi,

et al. 2024

Mathematics

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Image restoration technology is a crucial field in image processing and is extensively utilized across various domains. Recently, with advancements in graph convolutional network (GCN) technology, methods based on GCNs have increasingly been applied to image restoration, yielding impressive results. Despite these advancements, there is a gap in comprehensive research that consolidates various image denoising techniques. In this paper, we conduct a comparative study of image restoration techniques using GCNs. We begin by categorizing GCN methods into three primary application areas: image denoising, image super-resolution, and image deblurring. We then delve into the motivations and principles underlying various deep learning approaches. Subsequently, we provide both quantitative and qualitative comparisons of state-of-the-art methods using public denoising datasets. Finally, we discuss potential challenges and future directions, aiming to pave the way for further advancements in this domain. Our key findings include the identification of superior performance of GCN-based methods in capturing long-range dependencies and improving image quality across different restoration tasks, highlighting their potential for future research and applications.

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Effects of atmospheric turbulence on the imaging performance of optical system

Cited by 3 publications

References 9 publications

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

ARIES ST Radar: The First Central Himalayan Wind Profiler

Graph Convolutional Network for Image Restoration: A Survey

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