Comparison of common-path off-axis digital holography and transport of intensity equation in quantitative phase measurement

Tang, Ju; Zhang, Jiawei; Dou, Jiantai; Zhang, Jiwei; Di, Jianglei; Zhao, Jianlin

doi:10.1016/j.optlaseng.2022.107126

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…Hence, slowly varying phase structures hardly contribute to the intensity variations at the sensor plane, rendering them particularly difficult to recover. This has been the main drawback for in-line holography compared to off-axis holography [118,119]. Our findings suggest that the CCTV regularizer may be a competitive solution for this problem.…”

Section: Constraintmentioning

confidence: 85%

Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography

Gao¹,

Cao²

2023

gxjzz

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Holography provides access to the optical phase. The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring the signal priors. However, iterative projection methods based on physical knowledge of the wavefield suffer from poor imaging quality, whereas the regularization techniques sacrifice robustness for fidelity. In this work, we present a unified compressive phase retrieval framework for in-line holography that encapsulates the unique advantages of both physical constraints and sparsity priors. In particular, a constrained complex total variation (CCTV) regularizer is introduced that explores the well-known absorption and support constraints together with sparsity in the gradient domain, enabling practical high-quality in-line holographic imaging from a single intensity image. We developed efficient solvers based on the proximal gradient method for the non-smooth regularized inverse problem and the corresponding denoising subproblem. Theoretical analyses further guarantee the convergence of the algorithms with prespecified parameters, obviating the need for manual parameter tuning. As both simulated and optical experiments demonstrate, the proposed CCTV model can characterize complex natural scenes while utilizing physically tractable constraints for quality enhancement. This new compressive phase retrieval approach can be extended, with minor adjustments, to various imaging configurations, sparsifying operators, and physical knowledge. It may cast new light on both theoretical and empirical studies.

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Section: Constraintmentioning

confidence: 85%

Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography

Gao¹,

Cao²

2023

gxjzz

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Section: Dual-view Transport Of Intensity Phase Imagingmentioning

confidence: 99%

“…72 Moreover, due to its simple optical system and unwrapping-free advantage, transport of intensity phase imaging extends its applications in visible light bands, such as adaptive optics in astronomy 73,74 and optical imaging. [75][76][77][78][79][80][81][82][83] Like interferometry and coherent diffraction imaging, which code phase into fringe and diffraction pattern, respectively, transport of intensity phase imaging code phase into imaging intensity. In other words, a phase change distorts the wavefront and thus generates an inhomogeneity in the intensity distribution.…”

Section: Overview Of the Transport Of Intensity Phase Imagingmentioning

confidence: 99%

Dual-view transport of intensity phase imaging devices for quantitative phase microscopy applications

Wang,

Huang,

Sun

et al. 2024

Sens. Diagn.

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“…Speckle noise is a random phenomenon resulting from the interference of scattered wavefronts from rough surface, refractive inhomogeneities, multiple reflection, scratches, dust particle, and etc. [14][15][16]. Based on the principle of DH, the intensity of the recorded hologram 𝐼(𝑥, 𝑦) in two-dimensional coordinates 𝑥 and 𝑦 can be represented as follows:…”

Section: Speckle Noise In Digital Holographymentioning

confidence: 99%

Enhancing digital holography through an AI-powered approach for speckle noise reduction and coherence length preservation

Kim,

Jung,

Jang

et al. 2024

Practical Holography XXXVIII: Displays, Materials, and Applications

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Digital holography (DH) has become a promising tool in various research fields for acquiring quantitative phase information (QPI). However, its reliance on high-coherence light sources such as lasers often leads to speckle noise, which degrades image quality. Although low-coherence sources like light-emitting diodes (LEDs) can mitigate this noise, they struggle to create complete interference patterns for specimens with optical path differences exceeding their coherence length. This trade-off between high coherence and low speckle noise presents a significant challenge in DH, particularly in applications requiring long coherence lengths for accurate QPI. Our research addresses this challenge with an AIpowered approach. By training an AI model with paired hologram data from lasers and LEDs operating at the same peak wavelength, we have developed a method to reduce speckle noise while preserving the coherence length. The newly proposed method has been verified on reflective specimens using a Michelson interferometer. The resulting holograms from this AI model exhibit clear interference patterns over depths that match the laser's coherence length, while simultaneously achieving significantly reduced speckle noise, akin to that observed in LED holography.

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Comparison of common-path off-axis digital holography and transport of intensity equation in quantitative phase measurement

Cited by 9 publications

References 38 publications

Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography

Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography

Dual-view transport of intensity phase imaging devices for quantitative phase microscopy applications

Enhancing digital holography through an AI-powered approach for speckle noise reduction and coherence length preservation

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