Multi-source data fusion and super-resolution from astronomical images

Jalobeanu, A.; Gutiérrez, J. A.; Slezak, E.

doi:10.1016/j.stamet.2008.02.002

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Additionally, Medan et al [111] presented a Bayesian method to cross-match 5,827,988 high-proper-motion Gaia sources with various photometric surveys. Furthermore, Bayesian methods are employed to determine the probability of whether the data represent objects or the background in image fusion [112,113].…”

Section: Database Data Fusionmentioning

confidence: 99%

Artificial Intelligence in Astronomical Optical Telescopes: Present Status and Future Perspectives

Huang,

Hu,

Cai

et al. 2024

Universe

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With new artificial intelligence (AI) technologies and application scenarios constantly emerging, AI technology has become widely used in astronomy and has promoted notable progress in related fields. A large number of papers have reviewed the application of AI technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status of and research hotspots in telescope intelligence from these papers. This paper combines the development history of AI technology and difficulties with critical telescope technologies, comprehensively introduces the development of and research hotspots in telescope intelligence, conducts a statistical analysis of various research directions in telescope intelligence, and defines the merits of these research directions. A variety of research directions are evaluated, and research trends in each type of telescope intelligence are indicated. Finally, according to the advantages of AI technology and trends in telescope development, potential future research hotspots in the field of telescope intelligence are given.

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Section: Database Data Fusionmentioning

confidence: 99%

Artificial Intelligence in Astronomical Optical Telescopes: Present Status and Future Perspectives

Huang,

Hu,

Cai

et al. 2024

Universe

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“…When uncertain objects are combined into a single one (as it is the case in data fusion [2], or integrated data analysis [3]), correctly managing uncertainties is paramount. If the covariance matrix is diagonal, its elements act as weights and a simple weighted average is being computed.…”

Section: Introductionmentioning

confidence: 99%

Inverse covariance simplification for efficient uncertainty management

Jalobeanu

Gutiérrez

2007

AIP Conference Proceedings

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Abstract. When it comes to manipulating uncertain knowledge such as noisy observations of physical quantities, one may ask how to do it in a simple way. Processing corrupted signals or images always propagates the uncertainties from the data to the final results, whether these errors are explicitly computed or not. When such error estimates are provided, it is crucial to handle them in such a way that their interpretation, or their use in subsequent processing steps, remain user-friendly and computationally tractable. A few authors follow a Bayesian approach and provide uncertainties as an inverse covariance matrix. Despite its apparent sparsity, this matrix contains many small terms that carry little information. Methods have been developed to select the most significant entries, through the use of information-theoretic tools for instance. One has to find a Gaussian pdf that is close enough to the posterior pdf, and with a small number of non-zero coefficients in the inverse covariance matrix. We propose to restrict the search space to Markovian models (where only neighbors can interact), well-suited to signals or images. The originality of our approach is in conserving the covariances between neighbors while setting to zero the entries of the inverse covariance matrix for all other variables. This fully constrains the solution, and the computation is performed via a fast, alternate minimization scheme involving quadratic forms. The Markovian structure advantageously reduces the complexity of Bayesian updating (where the simplified pdf is used as a prior). Moreover, uncertainties exhibit the same temporal or spatial structure as the data.

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