Self-calibration for lensless color microscopy

Flasseur, Olivier; Fournier, Corinne; Verrier, Nicolas; Denis, Loïc; Jolivet, Frédéric; Cazier, Anthony; Lépine, Thierry

doi:10.1364/ao.56.00f189

Cited by 10 publications

(1 citation statement)

References 49 publications

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“…The choices of the Mie model and the Rayleigh-Sommerfeld propagation make the proposed method adapted to a large domain of validity extending to absorbing and/or dephasing spherical objects and transmittance planes without any restriction on the recording distance. Thus, other applications can be considered such as lens-free microscopy [4,5,7,34] or in-line microscopy in biology [11,14,24] where the Lorenz-Mie model fitting and the non-parametric reconstruction techniques have independently shown their efficiency. The proposed method would be particularly well suited to reconstruct samples in which spherical objects are present among objects of more complex shapes [35,36].…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of in-line holograms: combining model-based and regularized inversion

Berdeu¹,

Flasseur²,

Méès³

et al. 2019

Opt. Express

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In-line digital holography is a simple yet powerful tool to image absorbing and/or phase objects. Nevertheless, the loss of the phase of the complex wavefront on the sensor can be critical in the reconstruction process. The simplicity of the setup must thus be counterbalanced by dedicated reconstruction algorithms, such as inverse approaches, in order to retrieve the object from its hologram. In the case of simple objects for which the diffraction pattern produced in the hologram plane can be modeled using few parameters, a model fitting algorithm is very effective. However, such an approach fails to reconstruct objects with more complex shapes, and an image reconstruction technique is then needed. The improved flexibility of these methods comes at the cost of a possible loss of reconstruction accuracy. In this work, we combine the two approaches (model fitting and regularized reconstruction) to benefit from their respective advantages. The sample to be reconstructed is modeled as the sum of simple parameterized objects and a complex-valued pixelated transmittance plane. These two components jointly scatter the incident illumination, and the resulting interferences contribute to the intensity on the sensor. The proposed hologram reconstruction algorithm is based on alternating a model fitting step and a regularized inversion step. We apply this algorithm in the context of fluid mechanics, where holograms of evaporating droplets are analyzed. In these holograms, the high contrast fringes produced by each droplet tend to mask the diffraction pattern produced by the surrounding vapor wake. With our method, the droplet and the vapor wake can be jointly reconstructed.

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Section: Discussionmentioning

confidence: 99%

Reconstruction of in-line holograms: combining model-based and regularized inversion

Berdeu¹,

Flasseur²,

Méès³

et al. 2019

Opt. Express

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Wavelength-scanning pixel-super-resolved lens-free on-chip quantitative phase microscopy with a color image sensor

Wu,

Sun,

Chen

et al. 2024

APL Photonics

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We report a wavelength-scanning-based lens-free on-chip microscope using a color CMOS sensor and a matching modified phase retrieval algorithm for pixel super-resolution. Compared to traditional monochrome industrial cameras, color sensors favored by the consumer electronics industry have smaller pixel sizes, higher performance, and lower costs. However, the color filtering array (CFA) introduces inherent modulation to the holograms acquired under quasi-monochromatic illumination, which complicates the data processing in lens-free on-chip microscopy. Without physically removing the CFA positioned on the sensor chip, we demonstrate quantitative phase imaging (QPI) with a lateral half-width resolution of 615 nm over a wide field-of-view of 51.88 mm2 by exploiting the green-channel data from Bayer-masked holograms. The resulting spatial bandwidth product is 137.2 megapixels, over 10 times that of a conventional optical microscope. The rationale for using only green-channel data is that the information from each sampling point is not lost during propagation but rather distributed to all pixels in the image. Therefore, the missing data in other channels can be recovered by exploiting the sufficient differences among the raw images captured at different wavelengths. Compared to the scheme with monochrome sensors, this method requires the acquisition of several more images to guarantee the convergence of the algorithm. Experimental results show that we can achieve high-quality QPI performance, thus demonstrating the applicability of cost-effective color sensors in the field of lens-free holographic microscopy.

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Learning Image Formation and Regularization in Unrolling AMP for Lensless Image Reconstruction

Yang

Zhang

Yin

et al. 2022

IEEE Trans. Comput. Imaging

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Self-calibration for lensless color microscopy

Cited by 10 publications

References 49 publications

Reconstruction of in-line holograms: combining model-based and regularized inversion

Reconstruction of in-line holograms: combining model-based and regularized inversion

Wavelength-scanning pixel-super-resolved lens-free on-chip quantitative phase microscopy with a color image sensor

Learning Image Formation and Regularization in Unrolling AMP for Lensless Image Reconstruction

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