Penalized-likelihood image reconstruction for digital holography.
TLDR
A new numerical reconstruction approach using a statistical technique that reconstructs the complex field of the object from the real-valued hologram intensity data and derives an optimization transfer algorithm that monotonically decreases the cost function at each iteration.Abstract:
Conventional numerical reconstruction for digital holography using a filter applied in the spatial-frequency domain to extract the primary image may yield suboptimal image quality because of the loss in high-frequency components and interference from other undesirable terms of a hologram. We propose a new numerical reconstruction approach using a statistical technique. This approach reconstructs the complex field of the object from the real-valued hologram intensity data. Because holographic image reconstruction is an ill-posed problem, our statistical technique is based on penalized-likelihood estimation. We develop a Poisson statistical model for this problem and derive an optimization transfer algorithm that monotonically decreases the cost function at each iteration. Simulation results show that our statistical technique has the potential to improve image quality in digital holography relative to conventional reconstruction techniques.read more
Citations
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Journal ArticleDOI
Quantitative phase maps denoising of long holographic sequences by using SPADEDH algorithm
Pasquale Memmolo,Maria Iannone,Maurizio Ventre,Paolo A. Netti,Andrea Finizio,Melania Paturzo,Pietro Ferraro +6 more
TL;DR: This work proposes a denoising method for digital holography mod 2π wrapped phase map by using an adaptation of the SPArsity DEnoising of Digital Holograms (SPADEDH) algorithm, and proves that the proposed algorithm can be used as a helper for the typical local phase unwrapping algorithms.
Journal ArticleDOI
Digital holography super-resolution for accurate three-dimensional reconstruction of particle holograms
Nicolas Verrier,Corinne Fournier +1 more
TL;DR: This work suggests exploiting the information redundancy of videos to improve the reconstruction of the holograms by jointly estimating the position of the objects and the characteristic parameters.
Journal ArticleDOI
Fringe Pattern Improvement and Super-Resolution Using Deep Learning in Digital Holography
TL;DR: A deep learning-based method to super-resolve holograms and to improve the quality of low-resolution holograms by training a convolutional neural network with large-scale data for resolution enhancement is proposed.
Journal ArticleDOI
Regularized reconstruction of absorbing and phase objects from a single in-line hologram, application to fluid mechanics and micro-biology.
Frédéric Jolivet,Fabien Momey,Loïc Denis,Loïc Méès,Nicolas Faure,Nathalie Grosjean,Frédéric Pinston,Jean-Louis Marié,Corinne Fournier +8 more
TL;DR: This work proposes a regularized reconstruction method that includes several physically-grounded constraints such as bounds on transmittance values, maximum/minimum phase, spatial smoothness or the absence of any object in parts of the field of view and presents the promising results of reconstructions from experimental in-line holograms obtained in two different applications.
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Empirical concentration bounds for compressive holographic bubble imaging based on a Mie scattering model
TL;DR: The receiver operating characteristic (ROC) curves in this simulation provide an empirical concentration bound for accurate bubble detection by compressive holography at different noise levels, resulting in a maximum tolerable concentration much higher than the traditional back-propagation method.
References
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TL;DR: An improvement of the resolution by one decimal wotild require a correction of the objective to four decimals, a practically hopeless task.