Parametric blur estimation for blind restoration of atmospherically degraded images: Class G

Gao, Weizhe; Zhao, Xi-Le; Zou, Jianhua; Yang, Yikang; Xu, Rong; Zhang, Rongzhi; Xu, Xuebin

doi:10.1007/s10043-017-0312-4

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…We select four rays including

μ = 0

ν = 0

μ = ν

and

μ = - ν

to estimate

false(α, β false)

. In our previous work [16],

α

takes the arithmetic mean, and

β

takes the geometric mean. Here we use the data of the four rays together to estimate the OTF to improve accuracy and stability of our estimation.…”

Section: Blind Deconvolution Algorithmmentioning

confidence: 99%

Non‐iterative blind deconvolution algorithm based on power‐law distribution

Gao

Yang

et al. 2020

IET image process

Self Cite

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The spectral amplitude of most natural images is approximately isotropic and follows the power law. In this study, the authors propose a new non‐iterative blind image deconvolution algorithm that builds an isosceles curve model to approximate the spectrum amplitude of the real image. In the authors’ proposed algorithm, the optical transfer function (OTF) is obtained by comparing the reconstructed and degraded spectra. Then they employ the integrated multidirectional comprehensive estimation to reduce the OTF estimation error. The restored image is then obtained by applying the estimated OTF and the Wiener filter. Experiments on image deconvolution tasks indicate that the proposed algorithm provides a significant performance gain by obtaining an accurate OTF, reducing ringing artefacts compared with existing algorithms, and realising real‐time image restoration.

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“…We select four rays including

μ = 0

ν = 0

μ = ν

and

μ = - ν

to estimate

false(α, β false)

. In our previous work [16],

α

takes the arithmetic mean, and

β

takes the geometric mean. Here we use the data of the four rays together to estimate the OTF to improve accuracy and stability of our estimation.…”

Section: Blind Deconvolution Algorithmmentioning

confidence: 99%

Non‐iterative blind deconvolution algorithm based on power‐law distribution

Gao

Yang

et al. 2020

IET image process

Self Cite

View full text Add to dashboard Cite

show abstract

Out of focus multi-spectral image de-blurring using texture extraction and modified fourier transform

Iqbal

Riaz

Ghafoor

et al. 2021

Multimed Tools Appl

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Image restoration with point-spread function regularization and active learning

Jia,

Lv,

Ning

et al. 2023

Monthly Notices of the Royal Astronomical Society

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Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.

show abstract

Parametric blur estimation for blind restoration of atmospherically degraded images: Class G

Cited by 4 publications

References 22 publications

Non‐iterative blind deconvolution algorithm based on power‐law distribution

Non‐iterative blind deconvolution algorithm based on power‐law distribution

Out of focus multi-spectral image de-blurring using texture extraction and modified fourier transform

Image restoration with point-spread function regularization and active learning

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