The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Une méthode d'inpainting via une représentation clairsemée basée sur une nouvelle métrique de qualité sans phase est présentée dans cet article. Étant donné que les spectres de puissance et les caractéristiques sans phase des régions locales au sein des images permettent une représentation plus réussie de leurs caractéristiques de texture par rapport à leurs valeurs de pixels, une nouvelle métrique de qualité basée sur ces caractéristiques sans phase est nouvellement dérivée pour la représentation d'images. Plus précisément, le procédé proposé permet une représentation de rechange des signaux cibles, c'est-à-dire des correctifs cibles, y compris les intensités manquantes, en surveillant les erreurs convergentes par récupération de phase en tant que nouvelle métrique de qualité sans phase. C’est l’apport principal de notre étude. Dans cette approche, l'algorithme de récupération de phase utilisé dans notre méthode a les deux rôles importants suivants : (1) la dérivation de la nouvelle métrique de qualité qui peut être dérivée même pour les images incluant les intensités manquantes et (2) la conversion des caractéristiques sans phase, c'est-à-dire la puissance. spectres, aux valeurs de pixels, c'est-à-dire aux intensités. Par conséquent, la nouvelle approche ci-dessus résout le problème existant de l’impossibilité d’utiliser de meilleures fonctionnalités ou des métriques de meilleure qualité pour l’inpainting. Les résultats des expériences ont montré que la méthode proposée utilisant une représentation clairsemée basée sur la nouvelle métrique de qualité sans phase surpasse les méthodes précédemment rapportées qui utilisent directement les valeurs de pixels pour l'inpainting.
Takahiro OGAWA
Hokkaido University
Keisuke MAEDA
Hokkaido University
Miki HASEYAMA
Hokkaido University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Takahiro OGAWA, Keisuke MAEDA, Miki HASEYAMA, "Inpainting via Sparse Representation Based on a Phaseless Quality Metric" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 12, pp. 1541-1551, December 2020, doi: 10.1587/transfun.2020SMP0020.
Abstract: An inpainting method via sparse representation based on a new phaseless quality metric is presented in this paper. Since power spectra, phaseless features, of local regions within images enable more successful representation of their texture characteristics compared to their pixel values, a new quality metric based on these phaseless features is newly derived for image representation. Specifically, the proposed method enables spare representation of target signals, i.e., target patches, including missing intensities by monitoring errors converged by phase retrieval as the novel phaseless quality metric. This is the main contribution of our study. In this approach, the phase retrieval algorithm used in our method has the following two important roles: (1) derivation of the new quality metric that can be derived even for images including missing intensities and (2) conversion of phaseless features, i.e., power spectra, to pixel values, i.e., intensities. Therefore, the above novel approach solves the existing problem of not being able to use better features or better quality metrics for inpainting. Results of experiments showed that the proposed method using sparse representation based on the new phaseless quality metric outperforms previously reported methods that directly use pixel values for inpainting.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020SMP0020/_p
Copier
@ARTICLE{e103-a_12_1541,
author={Takahiro OGAWA, Keisuke MAEDA, Miki HASEYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Inpainting via Sparse Representation Based on a Phaseless Quality Metric},
year={2020},
volume={E103-A},
number={12},
pages={1541-1551},
abstract={An inpainting method via sparse representation based on a new phaseless quality metric is presented in this paper. Since power spectra, phaseless features, of local regions within images enable more successful representation of their texture characteristics compared to their pixel values, a new quality metric based on these phaseless features is newly derived for image representation. Specifically, the proposed method enables spare representation of target signals, i.e., target patches, including missing intensities by monitoring errors converged by phase retrieval as the novel phaseless quality metric. This is the main contribution of our study. In this approach, the phase retrieval algorithm used in our method has the following two important roles: (1) derivation of the new quality metric that can be derived even for images including missing intensities and (2) conversion of phaseless features, i.e., power spectra, to pixel values, i.e., intensities. Therefore, the above novel approach solves the existing problem of not being able to use better features or better quality metrics for inpainting. Results of experiments showed that the proposed method using sparse representation based on the new phaseless quality metric outperforms previously reported methods that directly use pixel values for inpainting.},
keywords={},
doi={10.1587/transfun.2020SMP0020},
ISSN={1745-1337},
month={December},}
Copier
TY - JOUR
TI - Inpainting via Sparse Representation Based on a Phaseless Quality Metric
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1541
EP - 1551
AU - Takahiro OGAWA
AU - Keisuke MAEDA
AU - Miki HASEYAMA
PY - 2020
DO - 10.1587/transfun.2020SMP0020
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2020
AB - An inpainting method via sparse representation based on a new phaseless quality metric is presented in this paper. Since power spectra, phaseless features, of local regions within images enable more successful representation of their texture characteristics compared to their pixel values, a new quality metric based on these phaseless features is newly derived for image representation. Specifically, the proposed method enables spare representation of target signals, i.e., target patches, including missing intensities by monitoring errors converged by phase retrieval as the novel phaseless quality metric. This is the main contribution of our study. In this approach, the phase retrieval algorithm used in our method has the following two important roles: (1) derivation of the new quality metric that can be derived even for images including missing intensities and (2) conversion of phaseless features, i.e., power spectra, to pixel values, i.e., intensities. Therefore, the above novel approach solves the existing problem of not being able to use better features or better quality metrics for inpainting. Results of experiments showed that the proposed method using sparse representation based on the new phaseless quality metric outperforms previously reported methods that directly use pixel values for inpainting.
ER -