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
A passagem aproximada generalizada de mensagens (GAMP) pode ser aplicada à recuperação de fase compressiva (CPR) com excelente comportamento de transição de fase. Neste artigo, introduzimos o modelo de textura de desenho animado na recuperação de fase baseada em remoção de ruído GAMP (D-prGAMP) e propusemos um algoritmo D-prGAMP (CT D-prGAMP) baseado em modelo de textura de desenho animado. Então, com base em experimentos e análises sobre as variações de desempenho dos algoritmos D-PrGAMP com iterações, propusemos um algoritmo D-prGAMP de 2 estágios, que faz compensações entre o algoritmo CT D-prGAMP e os algoritmos D-prGAMP gerais. Finalmente, enfrentando os problemas de não convergência do D-prGAMP, incorporamos o amortecimento adaptativo ao D-prGAMP de 2 estágios e propusemos o algoritmo D-prGAMP de 2 estágios com amortecimento adaptativo (ADD-prGAMP de 2 estágios). Os resultados da simulação mostram que o tempo de execução do D-prGAMP de 2 estágios é relativamente equivalente ao do BM3D-prGAMP, mas o D-prGAMP de 2 estágios pode alcançar maior qualidade de reconstrução de imagem do que o BM3D-prGAMP. O ADD-prGAMP de 2 estágios gasta mais tempo de reconstrução do que o D-prGAMP de 2 estágios e o BM3D-prGAMP. Porém, o ADD-prGAMP de 2 estágios pode atingir PSNRs 0.2 ~ 3dB maiores que os do D-prGAMP de 2 estágios e 0.3 ~ 3.1 dB maiores que os do 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 -