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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La transmission de messages approximatifs généralisés (GAMP) peut être appliquée à la récupération de phase compressive (CPR) avec un excellent comportement de transition de phase. Dans cet article, nous avons introduit le modèle de texture de dessin animé dans l'algorithme de récupération de phase basé sur le débruitage GAMP (D-prGAMP) et proposé un algorithme D-prGAMP (CT D-prGAMP) basé sur un modèle de texture de dessin animé. Ensuite, sur la base d'expérimentations et d'analyses sur les variations de performances des algorithmes D-PrGAMP avec des itérations, nous avons proposé un algorithme D-prGAMP en 2 étapes, qui fait des compromis entre l'algorithme CT D-prGAMP et les algorithmes généraux D-prGAMP. Enfin, face aux problèmes de non-convergence de D-prGAMP, nous avons incorporé l'amortissement adaptatif au D-prGAMP à 2 étages et proposé l'algorithme D-prGAMP à 2 étages à amortissement adaptatif (ADD-prGAMP à 2 étages). Les résultats de la simulation montrent que le temps d'exécution du D-prGAMP en 2 étapes est relativement équivalent à celui du BM3D-prGAMP, mais que le D-prGAMP en 2 étapes peut obtenir une qualité de reconstruction d'image supérieure à celle du BM3D-prGAMP. ADD-prGAMP en 2 étapes nécessite plus de temps de reconstruction que D-prGAMP et BM2D-prGAMP en 3 étapes. Mais l'ADD-prGAMP à 2 étages peut atteindre des PSNR 0.2 à 3 dB supérieurs à ceux du D-prGAMP à 2 étages et 0.3 à 3.1 dB supérieurs à ceux du BM3D-prGAMP.
Jingjing SI
Yanshan University,the Hebei Key Laboratory of Information Transmission and Signal Processing
Jing XIANG
Yanshan University,the Hebei Key Laboratory of Information Transmission and Signal Processing
Yinbo CHENG
Ocean College of Hebei Agricultural University
Kai LIU
Yanshan University,the Hebei Key Laboratory of Information Transmission and Signal Processing
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Jingjing SI, Jing XIANG, Yinbo CHENG, Kai LIU, "Compressive Phase Retrieval Realized by Combining Generalized Approximate Message Passing with Cartoon-Texture Model" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 9, pp. 1608-1615, September 2018, doi: 10.1587/transfun.E101.A.1608.
Abstract: Generalized approximate message passing (GAMP) can be applied to compressive phase retrieval (CPR) with excellent phase-transition behavior. In this paper, we introduced the cartoon-texture model into the denoising-based phase retrieval GAMP(D-prGAMP), and proposed a cartoon-texture model based D-prGAMP (C-T D-prGAMP) algorithm. Then, based on experiments and analyses on the variations of the performance of D-PrGAMP algorithms with iterations, we proposed a 2-stage D-prGAMP algorithm, which makes tradeoffs between the C-T D-prGAMP algorithm and general D-prGAMP algorithms. Finally, facing the non-convergence issues of D-prGAMP, we incorporated adaptive damping to 2-stage D-prGAMP, and proposed the adaptively damped 2-stage D-prGAMP (2-stage ADD-prGAMP) algorithm. Simulation results show that, runtime of 2-stage D-prGAMP is relatively equivalent to that of BM3D-prGAMP, but 2-stage D-prGAMP can achieve higher image reconstruction quality than BM3D-prGAMP. 2-stage ADD-prGAMP spends more reconstruction time than 2-stage D-prGAMP and BM3D-prGAMP. But, 2-stage ADD-prGAMP can achieve PSNRs 0.2∼3dB higher than those of 2-stage D-prGAMP and 0.3∼3.1dB higher than those of BM3D-prGAMP.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1608/_p
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@ARTICLE{e101-a_9_1608,
author={Jingjing SI, Jing XIANG, Yinbo CHENG, Kai LIU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Compressive Phase Retrieval Realized by Combining Generalized Approximate Message Passing with Cartoon-Texture Model},
year={2018},
volume={E101-A},
number={9},
pages={1608-1615},
abstract={Generalized approximate message passing (GAMP) can be applied to compressive phase retrieval (CPR) with excellent phase-transition behavior. In this paper, we introduced the cartoon-texture model into the denoising-based phase retrieval GAMP(D-prGAMP), and proposed a cartoon-texture model based D-prGAMP (C-T D-prGAMP) algorithm. Then, based on experiments and analyses on the variations of the performance of D-PrGAMP algorithms with iterations, we proposed a 2-stage D-prGAMP algorithm, which makes tradeoffs between the C-T D-prGAMP algorithm and general D-prGAMP algorithms. Finally, facing the non-convergence issues of D-prGAMP, we incorporated adaptive damping to 2-stage D-prGAMP, and proposed the adaptively damped 2-stage D-prGAMP (2-stage ADD-prGAMP) algorithm. Simulation results show that, runtime of 2-stage D-prGAMP is relatively equivalent to that of BM3D-prGAMP, but 2-stage D-prGAMP can achieve higher image reconstruction quality than BM3D-prGAMP. 2-stage ADD-prGAMP spends more reconstruction time than 2-stage D-prGAMP and BM3D-prGAMP. But, 2-stage ADD-prGAMP can achieve PSNRs 0.2∼3dB higher than those of 2-stage D-prGAMP and 0.3∼3.1dB higher than those of BM3D-prGAMP.},
keywords={},
doi={10.1587/transfun.E101.A.1608},
ISSN={1745-1337},
month={September},}
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TY - JOUR
TI - Compressive Phase Retrieval Realized by Combining Generalized Approximate Message Passing with Cartoon-Texture Model
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1608
EP - 1615
AU - Jingjing SI
AU - Jing XIANG
AU - Yinbo CHENG
AU - Kai LIU
PY - 2018
DO - 10.1587/transfun.E101.A.1608
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2018
AB - Generalized approximate message passing (GAMP) can be applied to compressive phase retrieval (CPR) with excellent phase-transition behavior. In this paper, we introduced the cartoon-texture model into the denoising-based phase retrieval GAMP(D-prGAMP), and proposed a cartoon-texture model based D-prGAMP (C-T D-prGAMP) algorithm. Then, based on experiments and analyses on the variations of the performance of D-PrGAMP algorithms with iterations, we proposed a 2-stage D-prGAMP algorithm, which makes tradeoffs between the C-T D-prGAMP algorithm and general D-prGAMP algorithms. Finally, facing the non-convergence issues of D-prGAMP, we incorporated adaptive damping to 2-stage D-prGAMP, and proposed the adaptively damped 2-stage D-prGAMP (2-stage ADD-prGAMP) algorithm. Simulation results show that, runtime of 2-stage D-prGAMP is relatively equivalent to that of BM3D-prGAMP, but 2-stage D-prGAMP can achieve higher image reconstruction quality than BM3D-prGAMP. 2-stage ADD-prGAMP spends more reconstruction time than 2-stage D-prGAMP and BM3D-prGAMP. But, 2-stage ADD-prGAMP can achieve PSNRs 0.2∼3dB higher than those of 2-stage D-prGAMP and 0.3∼3.1dB higher than those of BM3D-prGAMP.
ER -