Singular Value Decomposition in Image Compression and Blurred Image Restoration

Fronckova, Katerina; Pražák, Pavel; Slaby, Antonin

doi:10.1007/978-3-319-93000-8_8

Search citation statements

Order By: Relevance

Paper Sections

Select...

Singular Value Decomposition (Svd) Formalism1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

2023

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each element in the diagonal matrix corresponds to an image component. The technique is very effective at denoising [13][14][15] and compressing [16][17][18] a 2D image decomposed into separate variables, as in the case of the KAT-7 beam maps presented here. A detailed understanding of SVD can be obtained from [19].…”

Section: Singular Value Decomposition (Svd) Formalismmentioning

confidence: 99%

Denoising holographic measurement of primary beams. Part I. A singular value decomposition approach

et al. 2020

View full text Add to dashboard Cite

Radio astronomers are interested in measuring the intensity of the observed signal, and on that account, an accurate model of the primary beam is very crucial. However, holographic measurement of the primary beam is mostly corrupted by noise and this can impede the detection of weak emission from distant sources. The interference may be caused by antenna electronics, astrophysical sources, our cell phones or any other artificial electronics. This paper presents the Singular Value Decomposition (SVD) method to mitigate unwanted signal in perturbed primary beams. The results indicated that using 3 strongest coefficients of singular values, we can denoise and reproduce the image close to the original beam due to artefacts in the raw beam maps being reduced by a factor of at least 0.09%.

show abstract