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A novel variational approach for multiphoton microscopy image restoration: from PSF estimation to 3d deconvolution

INVERSE PROBLEMS 2024年第6期40卷 065003-065003页

作者： Ajdenbaum, Julien Chouzenoux, Emilie Lefort, Claire Martin, Segolene Pesquet, Jean-Christophe Univ Paris Saclay Ctr Vis Numer Inria Cent Supelec Gif Sur Yvette France Univ Limoges XLIM Res Inst CNRS UMR 7252 Limoges France

In multi-photon microscopy (MPM), a recent in-vivo fluorescence microscopy system, the task of image restoration can be decomposed into two interlinked inverse problems: firstly, the characterization of the point spread function (PSF) and subsequently, the deconvolution (i.e. deblurring) to remove the PSF effect, and reduce noise. The acquired MPM image quality is critically affected by PSF blurring and intense noise. The PSF in MPM is highly spread in 3d and is not well characterized, presenting high variability with respect to the observed objects. This makes the restoration of MPM images challenging. Common PSF estimation methods in fluorescence microscopy, including MPM, involve capturing images of sub-resolution beads, followed by quantifying the resulting ellipsoidal 3d spot. In this work, we revisit this approach, coping with its inherent limitations in terms of accuracy and practicality. We estimate the PSF from the observation of relatively large beads (approximately 1 mu m in diameter). This goes through the formulation and resolution of an original non-convex minimization problem, for which we propose a proximal alternating method along with convergence guarantees. Following the PSF estimation step, we then introduce an innovative strategy to deal with the high level multiplicative noise degrading the acquisitions. We rely on a heteroscedastic noise model for which we estimate the parameters. We then solve a constrained optimization problem to restore the image, accounting for the estimated PSF and noise, while allowing a minimal hyper-parameter tuning. Theoretical guarantees are given for the restoration algorithm. These algorithmic contributions lead to an end-to-end pipeline for 3d image restoration in MPM, that we share as a publicly available Python software. We demonstrate its effectiveness through several experiments on both simulated and real data.

关键词： non-convex optimisation multiphoton microscopy 3d deconvolution PSF calibration proximal algorithm

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Improving the depth resolution of STEM-AdF sectioning by 3d deconvolution

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MICROSCOPY 2021年第2期70卷 241-249页

作者： Ishizuka, A. Ishizuka, K. Ishikawa, R. Shibata, N. Ikuhara, Y. Hashiguchi, H. Sagawa, R. HREM Res Inc 14-48 Matsukazedai Higashimatsuyama Saitama Japan Univ Tokyo Inst Engn Innovat Bunkyo Ku 2-11-16 Yayoi Tokyo Japan Japan Sci & Technol Agcy PRESTO 4-1-8 Honcho Kawaguchi Saitama Japan Japan Fine Ceram Ctr Nanostruct Res Lab Nagoya Aichi Japan JEOL Ltd 3-1-2 Musashino Akishima Tokyo Japan

Although the possibility of locating single atom in three dimensions using the scanning transmission electron microscope (STEM) has been discussed with the advent of aberration correction technology, it is still a big challenge. In this report we have developed deconvolution routines based on maximum entropy method (MEM) and Richardson-Lucy algorithm (RLA), which are applicable to the STEM-annular dark-field (AdF) though-focus images to improve the depth resolution. The new three-dimensional (3d) deconvolution routines require a limited defocus-range of STEM-AdF images that covers a whole sample and some vacuum regions. Since the STEM-AdF probe is infinitely elongated along the optical axis, a 3d convolution is performed with a two-dimensional (2d) convolution over xy-plane using the 2d fast Fourier transform in reciprocal space, and a one-dimensional convolution along the z-direction in real space. Using our new deconvolution routines, we have processed simulated focal series of STEM-AdF images for single Ce dopants embedded in wurtzite-type AlN. Applying the MEM, the Ce peaks are clearly localized along the depth, and the peak width is reduced down to almost one half. We also applied the new deconvolution routines to experimental focal series of STEM-AdF images of a monolayer graphene. The RLA gives smooth and high-P/B ratio scattering distribution, and the graphene layer can be easily detected. Using our deconvolution algorithms, we can determine the depth locations of the heavy dopants and the graphene layer within the precision of 0.1 and 0.2 nm, respectively. Thus, the deconvolution must be extremely useful for the optical sectioning with 3d STEM-AdF images.

关键词： depth resolution optical sectioning STEM-AdF 3d deconvolution maximum entropy method (MEM) Richardson-Lucy algorithm

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A multi-step forecasting model of online car-hailing demand

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INFORMATION SCIENCES 2022年 587卷 572-586页

作者： Teng, Fei Teng, Jian Qiao, Lu du, Shengdong Li, Tianrui Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Peoples R China

Multi-step forecasting of online car-hailing demand is necessary for the long-term plan-ning of traffic resources. However, accurate forecasting is very challenging, because it is difficult to extract the dynamic spatial-temporal dependencies from high-dimensional nonlinear data. This paper proposes a regional traffic demand forecasting framework (RTdFF) to predict the boarding and alighting demand of online car-hailing. The RTdFF can learn robust representations of traffic patterns with multi-resolution spatial dependen-cies. It uses a 3d deconvolution component to enhance the spatial resolution of intra-regions and a 2d residual component to connect the large-scale spatial dependence. This model investigates the effect of the 3d convolution in capturing the long-term temporal dependence. Hybrid modeling is conducted with multi-granularity temporal segmentation and fusion. The experiments were carried out using real-world datasets. The results verify that RTdFF is accurate and stable, and that it has slower deterioration than baseline mod -els, with the raise of prediction steps. (C) 2021 Published by Elsevier Inc.

关键词： demand forecasting Urban big data 3d deconvolution Spatio-temporal dependence Multi -step prediction

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Susceptibility-based functional brain mapping by 3d deconvolution of an MR-phase activation map

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JOURNAL OF NEUROSCIENCE METHOdS 2013年第1期216卷 33-42页

作者： Chen, Zikuan Liu, Jingyu Calhoun, Vince d. Mind Res Network Albuquerque NM 87106 USA LBERI Albuquerque NM 87106 USA Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA

The underlying source of T2*-weighted magnetic resonance imaging (T2*MRI) for brain imaging is magnetic susceptibility (denoted by chi). T2*MRI outputs a complex-valued MR image consisting of magnitude and phase information. Recent research has shown that both the magnitude and the phase images are morphologically different from the source chi, primarily due to 3d convolution, and that the source chi can be reconstructed from complex MR images by computed inverse MRI (CIMRI). Thus, we can obtain a 4d chi dataset from a complex 4d MR dataset acquired from a brain functional MRI study by repeating CIMRI to reconstruct 3d chi volumes at each timepoint. Because the reconstructed chi is a more direct representation of neuronal activity than the MR image, we propose a method for chi-based functional brain mapping, which is numerically characterised by a temporal correlation map of chi responses to a stimulant task. Under the linear imaging conditions used for T2*MRI, we show that the chi activation map can be calculated from the MR phase map by CIMRI. We validate our approach using numerical simulations and Gd-phantom experiments. We also analyse real data from a finger-tapping visuomotor experiment and show that the chi-based functional mapping provides additional activation details (in the form of positive and negative correlation patterns) beyond those generated by conventional MR-magnitude-based mapping. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Functional brain mapping Temporal correlation (tcorr) 3d deconvolution Total variation (TV) regularisation Filter truncation Linear system theory Magnetic susceptibility tomography (chi tomography) T2*-weighted MRI (T2*MRI) Computed inverse MRI (CIMRI)

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Fast and robust wave optics-based reconstruction protocol for Fourier lightfield microscopy

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OPTICS ANd LASERS IN ENGINEERING 2023年 161卷

作者： Incardonaa, Nicolo Tolosa, Angel Saavedra, Genaro Martinez-Corral, Manuel Sanchez-Ortiga, Emilio Univ Valencia Dept Opt 3D Imaging & Display Lab Burjassot 46100 Spain Univ Europea Valencia Sch Sci Valencia 46010 Spain

Fourier lightfield microscopy (FLMic) is a powerful technique to record 3d images of thick dynamic samples. Belonging FLMic to the general class of computational imaging techniques, its efficiency is determined by sev-eral factors, like the optical system, the calibration process, the reconstruction algorithm, or the computation architecture. In the case of FLMic the calibration and the reconstruction algorithm should be fully adapted to the singular features of the technique. To this end, and concerning the reconstruction, we discard the use of experimental PSFs, and propose the use of a synthetic one, which is calculated on the basis of paraxial optics and taking into account the equal influence of diffraction and pixelation. Using this quite simple PSF, performing the adequate calibration and finally implementing the algorithm in GPU, we demonstrate here the possibility of obtaining 3d images with good results in terms of resolution and strong improvement in terms of computation time. In summary, and aiming to accelerate the widespread of FLMic among microscopy users and researchers, we are proposing a fast protocol fully adapted to FLMic and that is very flexible and robust against any slight misalignment or against the change of any optical element.

关键词： Fourier lightfield microscopy FLMic 3d microscopy 3d deconvolution GPU

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PURE-LET deconvolution OF 3d FLUORESCENCE MICROSCOPY IMAGES 14

PURE-LET DECONVOLUTION OF 3D FLUORESCENCE MICROSCOPY IMAGES

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IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Li, Jizhou Luisier, Florian Blu, Thierry Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Roche Diagnost Hematol Boston MA USA

ISBN: (纸本)9781509011728

Three-dimensional (3d) deconvolution microscopy is very effective in improving the quality of fluorescence microscopy images. In this work, we present an efficient approach for the deconvolution of 3d fluorescence microscopy images based on the recently developed PURE-LET algorithm. By combining multiple Wiener filtering and wavelet denoising, we parametrize the deconvolution process as a linear combination of elementary functions. Then the Poisson unbiased risk estimate (PURE) is used to obtain the optimal coefficients. The proposed approach is non-iterative and outperforms existing techniques (usually, variants of Richardson-Lucy algorithm) both in terms of computational efficiency and quality. We illustrate its effectiveness on both synthetic and real data.

关键词： 3d deconvolution fluorescence microscopy Poisson noise unbiased risk estimate

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A fast homotopy algorithm for gridless sparse recovery

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INVERSE PROBLEMS 2021年第2期37卷

作者： Courbot, Jean-Baptiste Colicchio, Bruno IRIMAS UR 7499 61 Rue Albert Camus F-68100 Mulhouse France

In this paper, we study the solving of the gridless sparse optimization problem and its application to 3d image deconvolution. Based on the recent works of [14] introducing the sliding Frank-Wolfe algorithm to solve the Beurling LASSO problem, we introduce an accelerated algorithm, denoted BSFW, that preserves its convergence properties, while removing most of the costly local descents. Besides, as the solving of BLASSO still relies on a regularization parameter, we introduce a homotopy algorithm to solve the constrained BLASSO that allows to use a more practical parameter based on the image residual, e.g. its standard deviation. Both algorithms benefit from a finite termination property, i.e. they are guaranteed to find the solution in a finite number of step under mild conditions. These methods are then applied on the problem of 3d tomographic diffractive microscopy images, with the purpose of explaining the image by a small number of atoms in convolved observations. Numerical results on synthetic and real images illustrates the improvement provided by the BSFW method, the homotopy method and their combination.

关键词： Beurling LASSO gridless sparse optimization homotopy algorithm 3d deconvolution tomographic diffractive microscopy

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Simultaneous recovery of both bright and dim structures from noisy fluorescence microscopy images using a modified TV constraint

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JOURNAL OF MICROSCOPY 2019年第1期275卷 24-35页

作者： Xiao, C. Smith, Z. J. Chu, K. Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei Anhui Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Hefei Anhui Peoples R China

The quality and information content of biological images can be significantly enhanced by postacquisition processing using deconvolution and denoising. However, when imaging complex biological samples, such as neurons, stained with fluorescence labels, the signal level of different structures can differ by several orders of magnitude. This poses a challenge as current image reconstruction algorithms are focused on recovering low signals and generally have sample-dependent performance, requiring tedious manual tuning. This is one of the main hurdles for their wide adoption by nonspecialists. In this work, we modify the general constrained reconstruction method (in our case utilizing a total variation constraint) so that both bright and dim structures can drive the deconvolution with equal force. In this way, we can simultaneously obtain high-quality reconstruction across a wide range of signals within a single image or image sequence. The algorithm is tested on both simulated and experimental data. When compared with current state-of-art algorithms, our algorithm outperforms others in terms of maintaining the resolution in the high-signal areas and reducing artefacts in the low-signal areas. The algorithm was also tested on image sequences where one set of parameters are used to reconstruct all images, with blind evaluation by a group of biologists demonstrating a marked preference for the images produced by our method. This means that our method is suitable for batch processing of image sequences obtained from either spatial or temporal scanning. Lay description Fluorescence microscopy images of complex biological samples contain a wide range of signal levels. This signal variation leads current reconstruction algorithms, which aim to enhance the quality of the raw images, to have sample-dependent performance. In this work, we design a new optimization that allows the reconstruction to "pay equal eqattention to" both bright and dim structures. In this way, we can si

关键词： 3d deconvolution fluorescence microscopy image recovery

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PURE-LET deconvolution OF 3d FLUORESCENCE MICROSCOPY IMAGES

PURE-LET DECONVOLUTION OF 3D FLUORESCENCE MICROSCOPY IMAGES

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International Symposium on Biomedical Imaging

作者： Jizhou Li Florian Luisier Thierry Blu Department of Electronic Engineering The Chinese University of Hong Kong Roche Diagnostics Hematology

ISBN: (纸本)9781509011735

关键词： 3d deconvolution fluorescence microscopy Poisson noise unbiased risk estimate

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FPGA-ACCELERATEd RICHARdSON-LUCY deconvolution FOR 3d IMAGE dATA 13

FPGA-ACCELERATED RICHARDSON-LUCY DECONVOLUTION FOR 3D IMAGE ...

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13th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Bromberger, Michael Bastian, Pascal Bergeest, Jan-Philip Conrad, Christian Heuveline, Vincent Rohr, Karl Karl, Wolfgang Karlsruhe Inst Technol Chair Comp Architecture & Parallel Proc CAPP D-76131 Karlsruhe Germany Heidelberg Inst Theoret Studies Data Min & Uncertainty Quantificat DMQ Grp D-69118 Heidelberg Germany Heidelberg Univ BIOQUANT IPMB D-69120 Heidelberg Germany Heidelberg Univ DKFZ Heidelberg Dept Bioinformat & Funct Genom D-69120 Heidelberg Germany

ISBN: (纸本)9781479923496;9781479923502

Efficient processing of 3d image data is one of the biggest challenges in biological image analysis. Single plane illumination microscopy, for instance, allows acquiring massive amounts of 3d image data that needs to be processed automatically. An important preprocessing step is deconvolution which reduces the image blur introduced by the imaging system. An often used algorithm is Richardson-Lucy deconvolution which, however, is very time-consuming for huge amounts of 3d image data. We have developed an FPGA-based acceleration of Richardson-Lucy deconvolution. Compared to CPU architectures the computation time is reduced.

关键词： 3d deconvolution field-programmable gate array (FPGA) heterogeneous system preprocessing of 3d image data

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