The Experts below are selected from a list of 20328 Experts worldwide ranked by ideXlab platform
Alessandro Mirone - One of the best experts on this subject based on the ideXlab platform.
-
Ring artifacts correction in compressed sensing Tomographic Reconstruction
Journal of synchrotron radiation, 2015Co-Authors: Pierre Paleo, Alessandro MironeAbstract:Ring artifacts are a very common problem in Tomographic Reconstruction, and numerous methods exist to either pre-process the sinogram or correct the reconstructed slice. A novel approach to perform the correction as part of the Reconstruction process is presented. It is shown that for iterative techniques, which amount to optimizing an objective function, the ring artifacts correction can be easily integrated in the formalism, enabling simultaneous slice Reconstruction and ring artifacts correction. This method is tested and compared with mainstream correction techniques for both simulated and experimental data. Results show that the correction is efficient, especially for undersampled datasets. This technique is included in the PyHST2 code which is used at the European Synchrotron Radiation Facility for Tomographic Reconstruction.
-
ring artifacts correction in compressed sensing Tomographic Reconstruction
arXiv: Computational Physics, 2015Co-Authors: Pierre Paleo, Alessandro MironeAbstract:We present a novel approach to handle ring artifacts correction in compressed sensing Tomographic Reconstruction. The correction is part of the Reconstruction process, which differs from classical sinogram pre-processing and image post-processing techniques. The principle of compressed sensing Tomographic Reconstruction is presented. Then, we show that the ring artifacts correction can be integrated in the Reconstruction problem formalism. We provide numerical results for both simulated and real data. This technique is included in the PyHST2 code which is used at the European Synchrotron Radiation Facility for Tomographic Reconstruction.
Pierre Paleo - One of the best experts on this subject based on the ideXlab platform.
-
Ring artifacts correction in compressed sensing Tomographic Reconstruction
Journal of synchrotron radiation, 2015Co-Authors: Pierre Paleo, Alessandro MironeAbstract:Ring artifacts are a very common problem in Tomographic Reconstruction, and numerous methods exist to either pre-process the sinogram or correct the reconstructed slice. A novel approach to perform the correction as part of the Reconstruction process is presented. It is shown that for iterative techniques, which amount to optimizing an objective function, the ring artifacts correction can be easily integrated in the formalism, enabling simultaneous slice Reconstruction and ring artifacts correction. This method is tested and compared with mainstream correction techniques for both simulated and experimental data. Results show that the correction is efficient, especially for undersampled datasets. This technique is included in the PyHST2 code which is used at the European Synchrotron Radiation Facility for Tomographic Reconstruction.
-
ring artifacts correction in compressed sensing Tomographic Reconstruction
arXiv: Computational Physics, 2015Co-Authors: Pierre Paleo, Alessandro MironeAbstract:We present a novel approach to handle ring artifacts correction in compressed sensing Tomographic Reconstruction. The correction is part of the Reconstruction process, which differs from classical sinogram pre-processing and image post-processing techniques. The principle of compressed sensing Tomographic Reconstruction is presented. Then, we show that the ring artifacts correction can be integrated in the Reconstruction problem formalism. We provide numerical results for both simulated and real data. This technique is included in the PyHST2 code which is used at the European Synchrotron Radiation Facility for Tomographic Reconstruction.
Robert Hovden - One of the best experts on this subject based on the ideXlab platform.
-
dynamic compressed sensing for real time Tomographic Reconstruction
Ultramicroscopy, 2020Co-Authors: Jonathan Schwartz, Yi Jiang, Huihuo Zheng, Marcus D Hanwell, Robert HovdenAbstract:Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D Reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, Reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.
Mitchell R Spearrin - One of the best experts on this subject based on the ideXlab platform.
-
volumetric laser absorption imaging of temperature co and co2 in laminar flames using 3d masked tikhonov regularization
Combustion and Flame, 2020Co-Authors: Chuyu Wei, Kevin K Schwarm, Daniel I Pineda, Mitchell R SpearrinAbstract:Abstract Mid-infrared laser absorption imaging of flame temperature and species concentration is expanded to three dimensions with linear Tomographic methods using Tikhonov regularization. A spatial convolution of two small-scale ( cm) laminar Bunsen-style flames, fueled by either ethylene or ethane, comprised the target flowfields. The flame doublets were alternately backlit with tunable radiation from a quantum cascade laser near 4.85 μ m and an interband cascade laser near 4.19 μ m to resolve rovibrational absorption transitions of carbon monoxide and carbon dioxide, respectively. 2D images were collected at 11 different projection angles, yielding an aggregate of 50,688 unique lines of sight capturing the scene with a pixel resolution of approximately 70 μ m. A linear Tomographic Reconstruction of absorbance areas with 2D Tikhonov regularization was performed using various numbers of projection angles and compared to a reference image based on an Abel inversion of a single axisymmetric flame. Increasing the number of projection angles improved the Tomographic Reconstruction with respect to resolving the voids, such as the inner core of the flame, though 11 projection angles still resulted in under-predicted void depths and Reconstruction artifacts outside of the flowfield. A 3D masked regularization method was then applied to further constrain the Tomographic Reconstruction in multiple dimensions and mitigate imaging artifacts. The introduction of the 3D regularization and mask were found to enhance spatial resolution of gradients within the flowfield and improve overall Reconstruction accuracy, effectively reducing the requisite number of projection angles.
-
2d mid infrared laser absorption imaging for Tomographic Reconstruction of temperature and carbon monoxide in laminar flames
Optics Express, 2019Co-Authors: Ryan J Tancin, Mitchell R Spearrin, Christopher S GoldensteinAbstract:This manuscript presents the design and initial application of a mid-infrared laser-absorption-imaging (LAI) technique for two-dimensional (2D) measurements and Tomographic Reconstruction of gas temperature and CO in laminar flames. In this technique, the output beam from a quantum-cascade laser (QCL) is expanded, passed through the test gas, and imaged in 2D using a high-speed mid-infrared camera. The wavelength of the QCL is scanned across the P(0,20) and P(1,14) transitions of CO near 4.8 μm at 50 Hz to provide 2D measurements of path-integrated gas temperature and CO column density across over 3,300 lines-of-sight simultaneously. This enabled the first sub-second (0.1 s), high-resolution (140 μm), 2D laser-absorption measurements and Tomographic Reconstruction of flame temperature and CO mole fraction using mid-infrared wavelengths. Prior to entering the test gas, the beam was reflected off two diffusers spinning at 90,000 RPM (≈9400 rad/s) to break the laser coherence and prevent diffraction-induced image artifacts. This technique was validated with measurements of CO in an isothermal jet and then demonstrated in laminar, partially premixed, oxygen-ethylene flames despite large background emission from soot and combustion products.
Jonathan Schwartz - One of the best experts on this subject based on the ideXlab platform.
-
dynamic compressed sensing for real time Tomographic Reconstruction
Ultramicroscopy, 2020Co-Authors: Jonathan Schwartz, Yi Jiang, Huihuo Zheng, Marcus D Hanwell, Robert HovdenAbstract:Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D Reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, Reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.
