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Exceeding the limits of algorithmic self-calibrated aberration recovery in Fourier ptychography

OPTICS CONTINUUM 2023年第1期2卷 119-130页

作者： Li, Eric Sherwin, Stuartq Gunjala, Gautam Waller, Laura Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Fourier ptychographic microscopy is a computational imaging technique that provides quantitative phase information and high resolution over a large field-of-view. Although the technique presents numerous advantages over conventional microscopy, model mismatch due to unknown optical aberrations can significantly limit reconstruction quality. A practical way of correcting for aberrations without additional data capture is through algorithmic self-calibration, in which a pupil recovery step is embedded into the reconstruction algorithm. However, software -only aberration correction is limited in accuracy. Here, we evaluate the merits of implementing a simple, dedicated calibration procedure for applications requiring high accuracy. In simulations, we find that for a target sample reconstruction error, we can image without any aberration corrections only up to a maximum aberration magnitude of lambda/40. When we use algorithmic self-calibration, we can tolerate an aberration magnitude up to lambda/10 and with our proposed diffuser calibration technique, this working range is extended further to lambda/3. Hence, one can trade off complexity for accuracy by using a separate calibration process, which is particularly useful for larger aberrations.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Computational imaging Image quality Imaging systems Optical aberrations Phase imaging reconstruction algorithms

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Three-dimensional flame temperature reconstruction through adaptive segmentation-weighted non-negative least squares and light field imaging

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Optical Engineering 2025年第05期64卷

作者： Tianxiang Ling Md. Moinul Hossain Guoqing Chen Qi Qi Biao Zhang Chuanlong Xu Southeast University (China) University of Kent (United Kingdom) China Energy Science and Technology Research Institute Co. Ltd. (China) State Key Laboratory of Low-Carbon Smart Coal-fired Power Generation and Ultra-clean Emission (China)

Existing flame temperature reconstruction algorithms experience significant performance degradation when subjected to radiation intensity noise interference, resulting in limited accuracy in low-temperature regions of flames with broad temperature distributions. We propose a three-dimensional (3D) flame temperature reconstruction algorithm by integrating adaptive segmentation-weighted non-negative least squares with light field imaging. Building on the non-negative least squares framework, the proposed algorithm introduces an adaptive strategy to improve the temperature reconstruction accuracy of low-temperature regions of flames. It also incorporates adaptive weight factors to reduce measurement errors caused by the extensive temperature range, enabling precise 3D temperature reconstruction. To validate the algorithm, numerical simulations of a bimodal asymmetric flame were performed to evaluate its noise tolerance and compare its performance with other existing algorithms. The simulation results indicated that the proposed algorithm demonstrates strong noise resistance, achieving ∼70% higher reconstruction accuracy than the least-square QR decomposition algorithm. Experiments were carried out on bimodal flames to reconstruct the temperature under various combustion conditions. The reconstructed temperature showed good agreement with trends reported in the literature. Our results demonstrate the viability and robustness of the proposed algorithm for reconstructing broader temperature distributions of flames.

关键词： Flame reconstruction algorithms Temperature metrology Temperature distribution Image segmentation Combustion Optical engineering Voxels Absorption Matrices

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Image reconstruction in Open-EIT by Combining Inversion Mapping and Sparse Contour Representation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Shen, Mengxian Sun, Jiangtao Suo, Peng Wang, Zhiying Wang, Yadong Tian, Wenbin Li, Changwen Xu, Lijun Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Chinese Acad Sci Key Lab Mech Fluid Solid Coupling Syst Inst Mech Beijing 100049 Peoples R China Beijing Inst Mech & Elect Engn Beijing 100000 Peoples R China China Agr Univ Coll Engn Beijing 100083 Peoples R China

This article presents an image reconstruction method for open electrical impedance tomography (OEIT) combined with inversion mapping and contour representation to address the profile and medium distribution measurement requirements of adhesion cavitation. Electrodes are arranged on the surface of an axisymmetric blunt body to form the Open-EIT sensor. To guarantee the accuracy of forward modeling in image reconstruction, the infinite domain outside the sensor is transformed into a disk by an equivalent dedicated inversion mapping. Then, the cavity contour is obtained using a customized B-spline-based sparse reconstruction method. Finally, the medium distribution to be reconstructed is confined within the contour to improve the imaging speed and resolution, which is then mapped back to the original infinity domain. Theoretical analysis and simulation demonstrate that the forward model maintains physical consistency after the inversion mapping, and the detection sensitivity is nearly ten times higher than that before the mapping. Simulations and experiments were conducted to verify the effectiveness of the proposed method, where the contour and its inside distribution can be better reconstructed in each specified case compared to other typical methods. The aforementioned method establishes a viable and effective approach for monitoring the cavitation occurrence on the surface of underwater vehicles.

关键词： Image reconstruction Sensors Electrodes Surface reconstruction Electrical impedance tomography reconstruction algorithms Mathematical models B-spline representation cavitation measurement inversion mapping open electrical impedance tomography (OEIT) region of interest (ROI) sparse reconstruction

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Effects of temporal coherence on EUV lensless imaging 38

Effects of temporal coherence on EUV lensless imaging

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Conference on Metrology, Inspection, and Process Control XXXVIII

作者： Fernandez, Sara Rajeev, Rajendran Helfenstein, Patrick Kazazis, Dimitrios Ekinci, Yasin Mochi, Iacopo Paul Scherrer Inst 111 Forsch Str Villigen Switzerland

ISBN: (纸本)9781510672178;9781510672161

One of the potential applications of coherent diffraction imaging (CDI) methods is at-wavelength defect inspection of extreme ultraviolet (EUV) photomasks for future semiconductor technology nodes. Mask defect metrology requires very high resolution as the size of relevant defects is 10 nm or less. In CDI, the resolution is limited by the highest detectable diffraction frequency component. The diffraction signal generated by a small isolated defect drops quickly as the diffraction angle increases and often long integration times are required to achieve a useful signal to noise ratio. With the introduction of states-mixtures into CDI methods, which has been intensively developed in recent years, it is possible to relax the CDI coherence requirements, and thus increase the illumination flux and reduce the exposure time for several types of EUV sources. In this paper, we systematically study the limits of temporal coherence on CDI using EUV synchrotron light and imaging of EUV masks in the reflection mode and we quantify the potential throughput increase for EUV photomask metrology applications.

关键词： Image restoration Temporal coherence Extreme ultraviolet Image quality Light sources and illumination Diffraction Optical coherence Photomasks reconstruction algorithms Scanning electron microscopy

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Virtual Conjugate Coil for Improving KerNL reconstruction 44

Virtual Conjugate Coil for Improving KerNL Reconstruction

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Chang, Yuchou Zhang, Jiming Pham, Huy Anh Li, Zhiqiang Lyu, Jingyuan Univ Massachusetts Dartmouth Dept Comp & Informat Sci N Dartmouth MA 02047 USA Univ Vermont Med Ctr Dept Radiol Oncol & Med Phys Burlington VT 05405 USA Univ Vermont Robert Lamer Coll Med Burlington VT 05405 USA 8100 Cambridge St Houston TX 77054 USA Barrow Neurol Inst Dept Neuroradol Phoenix AZ 85013 USA UIH Amer Inc Houston TX 77054 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

KerNL is a general kernel-based framework for autocalibrated reconstruction method, which does not need any explicit formulas of the kernel function for characterizing nonlinear relationships between acquired and unacquired k-space data. It is non-iterative without requiring a large amount of computational costs. Since the limited autocalibration signals (ACS) are acquired to perform KerNL calibration and the calibration suffers from the overfitting problem, more training data can improve the kernel model accuracy. In this work, virtual conjugate coil data are incorporated into the KerNL calibration and estimation process for enhancing reconstruction performance. Experimental results show that the proposed method can further suppress noise and aliasing artifacts with fewer ACS data and higher acceleration factors. Computation efficiency is still retained to keep fast reconstruction with the random projection.

关键词： Magnetic resonance imaging Training data Estimation reconstruction algorithms Encoding Calibration Computational efficiency

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Real-time Semantic 3D reconstruction for High-Touch Surface Recognition for Robotic Disinfection

Real-time Semantic 3D Reconstruction for High-Touch Surface ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Qiu, Ri-Zhao Sun, Yixiao Marques, Joao Marcos Correia Hauser, Kris Univ Illinois Dept Comp Sci Urbana IL 61801 USA Stanford Univ Dept Mech Engn Stanford CA 94305 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Disinfection robots have applications in promoting public health and reducing hospital acquired infections and have drawn considerable interest due to the COVID-19 pandemic. To disinfect a room quickly, motion planning can be used to plan robot disinfection trajectories on a reconstructed 3D map of the room's surfaces. However, existing approaches discard semantic information of the room and, thus, take a long time to perform thorough disinfection. Human cleaners, on the other hand, disinfect rooms more efficiently by prioritizing the cleaning of high-touch surfaces. To address this gap, we present a novel GPU-based volumetric semantic TSDF (Truncated Signed Distance Function) integration system for semantic 3D reconstruction. Our system produces 3D reconstructions that distinguish high-touch surfaces from non-high-touch surfaces at approximately 50 frames per second on a consumer-grade GPU, which is approximately 5 times faster than existing CPU-based TSDF semantic reconstruction methods. In addition, we extend a UV disinfection motion planning algorithm to incorporate semantic awareness for optimizing coverage of disinfection trajectories. Experiments show that our semantic-aware planning outperforms geometry-only planning by disinfecting up to 20% more high-touch surfaces under the same time budget. Further, the real-time nature of our semantic reconstruction pipeline enables future work on simultaneous disinfection and mapping.

关键词： Surface reconstruction Three-dimensional displays Semantics Pipelines reconstruction algorithms Real-time systems Planning

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Subaperture Motion-Adaptive reconstruction Techniques for Digital Beamforming Airborne SAR

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Castillo, Juan Pablo Navarro Scheiber, Rolf Jaeger, Marc Moreira, Alberto German Aerosp Ctr DLR Microwaves & Radar Inst D-82234 Wessling Germany

The use of airborne synthetic aperture radar (SAR) to demonstrate high-resolution wide swath (HRWS) operational modes in spaceborne SAR missions has supported the development of advanced digital beamforming (DBF) techniques. In doing so, one of the challenges to overcome is the temporal variation of the antenna phase centers in the airborne DBF SAR scenario, which significantly degrades the performance of the azimuth reconstruction. Multiple motion compensation (MoCo) solutions have been explored to correct these inconsistencies. However, the compensation of residual phase errors in the Doppler domain remains unresolved when the multichannel data has an aliased azimuth spectrum. This article proposes an algorithm that exploits the properties of the DBF azimuth reconstruction to correct these residual motion inconsistencies, although the channels are undersampled. The algorithm modifies the input range-compressed multichannel data by using an innovative MoCo technique to compensate the phase components coming from undesired 3-D time-variant baselines between the different apertures. Furthermore, a two-step azimuth reconstruction configuration is implemented to account for the polychromatic nature of SAR signals. To test the performance of the algorithm, point target simulations were carried out, in which the impact of a realistic across-track motion, inaccuracies in the digital elevation model (DEM), and variable velocity are analyzed. The results confirm the efficacy of the proposed technique in azimuth ambiguity suppression, where excellent ambiguity suppression is observed after applying the proposed MoCo technique. Finally, the outcome of the simulations is validated with real multichannel data acquired by the German Aerospace Center (DLR) airborne DBFSAR system.

关键词： Azimuth Image reconstruction Filters reconstruction algorithms Synthetic aperture radar Noise Doppler effect Antennas Transmitters Time-domain analysis Airborne synthetic aperture radar (SAR) azimuth ambiguities azimuth reconstruction digital beamforming (DBF) motion compensation (MoCo) velocity variation

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A generic model of semiconductor-based photon-counting detectors for spectral CT

A generic model of semiconductor-based photon-counting detec...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Bhattarai, Mridul Panta, Raj Kumar Clark, Darin Segars, W. Paul Abadi, Ehsan Samei, Ehsan Duke Univ Med Phys Grad Program Durham NC 27708 USA Duke Univ Sch Med Dept Radiol Ctr Virtual Imaging Trials CVIT Durham NC 27708 USA Duke Univ Sch Med Dept Radiol Quantitat Imaging & Anal Lab Durham NC 27708 USA

ISBN: (纸本)9781510671553;9781510671546

Photon-counting CT (PCCT) is an emerging x-ray spectral imaging technology. There are several PCCT prototypes available for clinical and experimental applications. Despite many published results, objective quantitative evaluation of PCCT systems remains challenging due to varied and unknown influences. In addition, CT acquisition geometry and image processing mechanisms are often proprietary, complicating the correlations between the detector's physical characteristics and the corresponding CT image quality, hindering fair comparisons between different technologies. This study aimed to provide a resource to address these challenges by developing a versatile model that accurately replicates the physics of any semiconductor-based photon-counting detector (PCD) and integrating it into a virtual imaging framework to generate detector-specific CT images. The methodology involved a Monte Carlo simulation to model x-ray photon interactions with PCDs and an analytical Gaussian charge sharing model to model charge-diffusion and -repulsion in the detector. Finally, the energy deposited post-crosstalk by source photons (1-120 keV) in the 3x3-pixel-neighborhood was translated into photon counts across two energy thresholds to compute detector-specific spatio-energetic covariance correlation matrices. This framework was validated against experimental measurements of a clinical CdTe-based PCCT, showcasing its accuracy in reproducing real-world scenarios. Additionally, to demonstrate the model's efficacy, we simulated key PCD materials (Si, CdTe, CZT) and designed standardized scanner components, developing virtual systems (DukeCounter scanners). These systems were used to "scan" a physics phantom (ACR) and anthropomorphic-computational models (XCAT). In low-energy (i.e., 5 keV for Si, 20 keV for CdTe and CZT) threshold images, for 125 mAs acquisitions, (noise magnitudes, HU for bone insert) measured in ACR phantom were (76.0 HU, 889.4 HU), (76.9 HU, 932.9 HU), (81.7 HU, 939.4 HU)

关键词： Sensors Silicon Computed tomography Image quality Monte Carlo methods X-ray computed tomography Scanners Systems modeling reconstruction algorithms Semiconductors Cadmium telluride Photon counting

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Terahertz Image reconstruction using Compressive Sensing 47

Terahertz Image Reconstruction using Compressive Sensing

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47th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

作者： Latha, A. Mercy Esampelly, Swapna Devi, A. S. Nirmala CSIR Cent Elect Engn Res Inst CSIR CEERI Chennai Ctr Chennai Tamil Nadu India Maulana Azad Natl Inst Technol Dept Phys Nanotechnol Bhopal India Council Sci & Ind Res CSIR Headquarters New Delhi India

ISBN: (纸本)9781728194271

The major limitation of the conventional terahertz (THz) imaging system based on a single-pixel detector is the high image acquisition time inhibiting its real-time applications. Alternatively, to reduce the image acquisition time, compressive sensing has been widely explored, wherein a reduced number of imaging points are employed to reconstruct the image. However, this technique, being extremely sensitive to the selection and ordering of the imaging masks, demands a well-formulated and efficient reconstruction algorithm. In the present work, the authors have extensively investigated the reconstruction of the THz images using a total variation minimization algorithm for different compression rates, imaging masks and optimization parameters.

关键词： Terahertz wave imaging Image coding Detectors reconstruction algorithms Minimization Real-time systems Image reconstruction

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Quantitative imaging of Iodine-based contrast agent in dual-energy DBT 17

Quantitative imaging of Iodine-based contrast agent in dual-...

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17th International Workshop on Breast Imaging (IWBI)

作者： Sidky, Emil Y. Wu, Xiangyi Duan, Xiaoyu Huang, Hailiang Zhao, Wei Zhang, Leo Y. Phillips, John Paul Zhang, Zheng Chen, Buxin Xia, Dan Reiser, Ingrid S. Pan, Xiaochuan Univ Chicago Dept Radiol MC 20265841 S Maryland Ave Chicago IL 60637 USA SUNY Stony Brook Renaissance Sch Med Dept Radiol Hlth Sci Ctr 101 Nicolls RdLevel 4 Stony Brook NY 11794 USA Univ Washington Dept Math Box 354350C 138 Padelford Seattle WA 98195 USA

ISBN: (数字)9781510680210

ISBN: (纸本)9781510680210;9781510680203

We report on an optimization-based image reconstruction algorithm for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm is designed to enable quantitative imaging of Iodine-based contrast agent by mitigating the depth blur artifact. The depth blurring is controlled by exploiting gradient sparsity of the contrast agent distribution. We find that minimization of directional total variation (TV) is particularly effective at exploiting gradient sparsity for the DBT scan configuration. In this initial work, the contrast agent imaging is performed by reconstructing images from DBT data acquired at source potentials of 30 and 49 kV, followed by weighted subtraction to suppress background glandular structure and isolate the contrast agent distribution. The algorithm is applied to DBT data, acquired with a Siemens Mammomat scanner, of a structured breast phantom with Iodine contrast agent inserts. Results for both in-plane and transverse-plane imaging for directional TV minimization are presented alongside images reconstructed by filtered back-projection for reference. It is seen that directional TV is able to substantially reduce depth blur for the Iodine-based contrast agent objects.

关键词： Digital breast tomosynthesis Contrast agents reconstruction algorithms Tumors Image processing Iodine

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