Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization

This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstru...
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Autor*in:	Xing Qin [verfasserIn] Xin Gao [verfasserIn] Xiaoxu Yang [verfasserIn] Meilin Xie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	phase retrieval incomplete magnitudes wavelet decomposition alternative directional multiplier method

Übergeordnetes Werk:	In: Photonics - MDPI AG, 2014, 11(2024), 1, p 71
Übergeordnetes Werk:	volume:11 ; year:2024 ; number:1, p 71

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/photonics11010071

Katalog-ID:	DOAJ096307552

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spelling	10.3390/photonics11010071 doi (DE-627)DOAJ096307552 (DE-599)DOAJ2f19af20caf747afb318348d13dc135f DE-627 ger DE-627 rakwb eng TA1501-1820 Xing Qin verfasserin aut Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt–pepper noise. phase retrieval incomplete magnitudes wavelet decomposition alternative directional multiplier method Applied optics. Photonics Xin Gao verfasserin aut Xiaoxu Yang verfasserin aut Meilin Xie verfasserin aut In Photonics MDPI AG, 2014 11(2024), 1, p 71 (DE-627)786192763 (DE-600)2770002-1 23046732 nnns volume:11 year:2024 number:1, p 71 https://doi.org/10.3390/photonics11010071 kostenfrei https://doaj.org/article/2f19af20caf747afb318348d13dc135f kostenfrei https://www.mdpi.com/2304-6732/11/1/71 kostenfrei https://doaj.org/toc/2304-6732 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2024 1, p 71
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language	English
source	In Photonics 11(2024), 1, p 71 volume:11 year:2024 number:1, p 71
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	phase retrieval incomplete magnitudes wavelet decomposition alternative directional multiplier method Applied optics. Photonics
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container_title	Photonics
authorswithroles_txt_mv	Xing Qin @@aut@@ Xin Gao @@aut@@ Xiaoxu Yang @@aut@@ Meilin Xie @@aut@@
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callnumber-first	T - Technology
author	Xing Qin
spellingShingle	Xing Qin misc TA1501-1820 misc phase retrieval misc incomplete magnitudes misc wavelet decomposition misc alternative directional multiplier method misc Applied optics. Photonics Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization
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topic_title	TA1501-1820 Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization phase retrieval incomplete magnitudes wavelet decomposition alternative directional multiplier method
topic	misc TA1501-1820 misc phase retrieval misc incomplete magnitudes misc wavelet decomposition misc alternative directional multiplier method misc Applied optics. Photonics
topic_unstemmed	misc TA1501-1820 misc phase retrieval misc incomplete magnitudes misc wavelet decomposition misc alternative directional multiplier method misc Applied optics. Photonics
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title	Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization
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title_full	Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization
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title_sort	complex noise-based phase retrieval using total variation and wavelet transform regularization
callnumber	TA1501-1820
title_auth	Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization
abstract	This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt–pepper noise.
abstractGer	This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt–pepper noise.
abstract_unstemmed	This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt–pepper noise.
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title_short	Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization
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author2	Xin Gao Xiaoxu Yang Meilin Xie
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