Image Restoration with Point Spread Function Regularization and Active Learning Article Swipe

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Peng Jia , Jiameng Lv , Runyu Ning , Yu Song , Nan Li , Kaifan Ji , Chenzhou Cui , Shanshan Li ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2311.00186

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal 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 directly restore images obtained by the telescope, 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 LSST, Euclid, and CSST, to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.

Related Topics

Computer Science

Fidelity

Point Spread Function

Artificial Intelligence

Concepts

Computer science Fidelity Point spread function Artificial intelligence Artificial neural network Sky Noise (video) Computer vision Regularization (linguistics) Point (geometry) Scale (ratio) Image restoration Image (mathematics) Image processing Mathematics Physics Astrophysics Telecommunications Quantum mechanics Geometry

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2311.00186
PDF: https://arxiv.org/pdf/2311.00186
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4388275140

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DOI: https://doi.org/10.48550/arxiv.2311.00186

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Title: Image Restoration with Point Spread Function Regularization and Active Learning

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-10-31

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Authors: Peng Jia, Jiameng Lv, Runyu Ning, Yu Song, Nan Li, Kaifan Ji, Chenzhou Cui, Shanshan Li

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Landing page: https://arxiv.org/abs/2311.00186

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PDF URL: https://arxiv.org/pdf/2311.00186

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Open access: Yes

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OA status: green

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Concepts: Computer science, Fidelity, Point spread function, Artificial intelligence, Artificial neural network, Sky, Noise (video), Computer vision, Regularization (linguistics), Point (geometry), Scale (ratio), Image restoration, Image (mathematics), Image processing, Mathematics, Physics, Astrophysics, Telecommunications, Quantum mechanics, Geometry

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Cited by: 1

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Citations by year (recent): 2024: 1

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Related works (count): 10

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abstract_inverted_index.training	85
abstract_inverted_index.Analysing	14
abstract_inverted_index.algorithm	70, 77, 132, 159
abstract_inverted_index.celestial	8
abstract_inverted_index.different	92
abstract_inverted_index.functions	47
abstract_inverted_index.generates	89
abstract_inverted_index.including	10
abstract_inverted_index.increases	152
abstract_inverted_index.intricate	21
abstract_inverted_index.promoting	187
abstract_inverted_index.research.	194
abstract_inverted_index.simulated	136
abstract_inverted_index.simulator	88
abstract_inverted_index.telescope	81
abstract_inverted_index.training,	111
abstract_inverted_index.efficiency	53, 183
abstract_inverted_index.evolution,	36
abstract_inverted_index.extraction	56
abstract_inverted_index.processing	16
abstract_inverted_index.simulator.	82, 127
abstract_inverted_index.structures	23, 147
abstract_inverted_index.telescope,	122
abstract_inverted_index.Large-scale	0
abstract_inverted_index.effectively	144
abstract_inverted_index.extraction,	186
abstract_inverted_index.information	55, 185
abstract_inverted_index.large-scale	164
abstract_inverted_index.morphology,	35
abstract_inverted_index.observation	137, 156
abstract_inverted_index.properties.	39
abstract_inverted_index.represented	124
abstract_inverted_index.researchers	28
abstract_inverted_index.restoration	69, 76
abstract_inverted_index.astronomical	1, 193
abstract_inverted_index.comprehensive	31
abstract_inverted_index.high-fidelity	80
abstract_inverted_index.learning-based	75
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	8
sustainable_development_goals[0].id	https://metadata.un.org/sdg/11
sustainable_development_goals[0].score	0.6600000262260437
sustainable_development_goals[0].display_name	Sustainable cities and communities
citation_normalized_percentile