Application of GRNN neural network in non-texture image inpainting and restoration
• We model an inpainting method based on GRNN neural network. • The missing regions are separated and sorted according to their size. • The missing regions are determined by performing regression analysis. • We use the magnitude of the gradient of the image to determine the spread parameter. • For c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
K. Alilou, Vahid [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Thermal structure optimization of a supercondcuting cavity vertical test cryostat - Jin, Shufeng ELSEVIER, 2019, an official publ. of the International Association for Pattern Recognition, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:62 ; year:2015 ; day:1 ; month:09 ; pages:24-31 ; extent:8 |
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| DOI / URN: |
10.1016/j.patrec.2015.04.020 |
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| 520 | |a • We model an inpainting method based on GRNN neural network. • The missing regions are separated and sorted according to their size. • The missing regions are determined by performing regression analysis. • We use the magnitude of the gradient of the image to determine the spread parameter. • For color images, the YCbCr color space is employed. | ||
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Application of GRNN neural network in non-texture image inpainting and restoration |
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• We model an inpainting method based on GRNN neural network. • The missing regions are separated and sorted according to their size. • The missing regions are determined by performing regression analysis. • We use the magnitude of the gradient of the image to determine the spread parameter. • For color images, the YCbCr color space is employed. |
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• We model an inpainting method based on GRNN neural network. • The missing regions are separated and sorted according to their size. • The missing regions are determined by performing regression analysis. • We use the magnitude of the gradient of the image to determine the spread parameter. • For color images, the YCbCr color space is employed. |
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• We model an inpainting method based on GRNN neural network. • The missing regions are separated and sorted according to their size. • The missing regions are determined by performing regression analysis. • We use the magnitude of the gradient of the image to determine the spread parameter. • For color images, the YCbCr color space is employed. |
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