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Iterative reconstruction algorithm based on discriminant adaptive-weighted TV regularization for fibrous biological tissues using in-line X-ray phase-contrast imaging

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BIOMEDICAL OPTICS EXPRESS 2021年第4期12卷 2460-2483页

作者： Zheng, Mengting Zhao, Yuqing Han, Shuo Ji, Dongjiang Li, Yimin Lv, Wenjuan Xin, Xiaohong Zhao, Xinyan Hu, Chunhong Tianjin Med Univ Sch Biomed Engn & Technol Tianjin 300070 Peoples R China Tianjin Univ Technol & Educ Sch Sci Tianjin 300222 Peoples R China Capital Med Univ Beijing Friendship Hosp Liver Res Ctr Beijing 100050 Peoples R China

In-line X-ray phase-contrast computed tomography (IL-PCCT) can produce highcontrast and high-resolution images of biological samples, and it has a great advantage with regard to imaging the microstructures and morphologies of fibrous biological tissues (FBTs). Filtered back projection (FBP) is widely used in ILPCCT. However, it requires long scanning times and high radiation doses to produce high-quality CT images, and this restricts its applicability in biomedical and preclinical studies on FBTs. To solve this problem, a novel IL-PCCT reconstruction algorithm is proposed to decrease the radiation dose by reducing the number of projections and reconstruct high-quality CT images of FBTs. The proposed algorithm incorporates the FBP method into the iterative reconstruction framework. Considering the area types and anisotropic edge properties of FBTs, a discriminant adaptive-weighted total variation model is introduced to optimize the intermediate reconstructed images. A fibrous phantom simulation and real experiment were performed to assess the performance of the proposed algorithm. Simulation and experimental results demonstrated that the proposed algorithm is an effective IL-PCCT reconstruction method for FBTs with incomplete projection data, and it has a great ability to suppress artifacts and preserve the edges of fibrous structures.

关键词： Biological imaging Computed tomography Image quality Image reconstruction reconstruction algorithms X ray imaging

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PPG Signal reconstruction Using Deep Convolutional Generative Adversarial Network 44

PPG Signal Reconstruction Using Deep Convolutional Generativ...

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

作者： Wang, Yuning Azimi, Iman Kazemi, Kianoosh Rahmani, Amir M. Liljeberg, Pasi Univ Turku Dept Comp Turku Finland Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA USA Univ Calif Irvine Dept Comp Sci Irvine CA USA Univ Calif Irvine Sch Nursing Irvine CA USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to collect various vital signs, including heart rate and heart rate variability. The signal is highly susceptible to motion artifacts, which is inevitable in health monitoring and may lead to inaccurate decision-making. Studies in the literature proposed time series analysis, signal decomposition, and machine learning methods to reconstruct PPG signals or reduce noise. However, they are limited to short-term noisy signals or to noise caused by certain physical activities. In this paper, we propose a deep convolutional generative adversarial network (GAN) method to reconstruct distorted PPG signals. Our method exploits the temporal information extracted from the corrupted signal and preceding data to perform PPG reconstruction. The model is trained and tested using data collected by smartwatches in a home-based health monitoring application. We evaluate the proposed GAN method in comparison to three state-of-the-art PPG reconstruction methods. The evaluation includes noisy PPG signals with different durations and SNR values. The proposed method outperforms the other methods by obtaining the least error rates. The results indicate that the proposed method is effective for improving PPG signal quality to produce reliable heart rate and heart rate variability.

关键词： reconstruction algorithms Generative adversarial networks Distortion Noise measurement Reliability Heart rate variability Signal resolution

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Deep Hyperspectral-Depth reconstruction Using Single Color-Dot Projection

Deep Hyperspectral-Depth Reconstruction Using Single Color-D...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Chunyu Monno, Yusuke Okutomi, Masatoshi Tokyo Inst Technol Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Depth reconstruction and hyperspectral reflectance reconstruction are two active research topics in computer vision and image processing. Conventionally, these two topics have been studied separately using independent imaging setups and there is no existing method which can acquire depth and spectral reflectance simultaneously in one shot without using special hardware. In this paper;we propose a novel single-shot hyperspectral-depth reconstruction method using an off-the-shelf RGB camera and projector. Our method is based on a single color-dot projection, which simultaneously acts as structured light for depth reconstruction and spatially-varying color illuminations for hyperspectral reflectance reconstruction. To jointly reconstruct the depth and the hyperspectral reflectance from a single color-dot image, we propose a novel end-to-end network architecture that effectively incorporates a geometric color-dot pattern loss and a photometric hyperspectral reflectance loss. Through the experiments, we demonstrate that our hyperspectral-depth reconstruction method outperforms the combination of an existing state-of-the-art single-shot hyperspectral reflectance reconstruction method and depth reconstruction method.

关键词： Reflectivity Training Computer vision Three-dimensional displays reconstruction algorithms Cameras Sensors

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Neural Template: Topology-aware reconstruction and Disentangled Generation of 3D Meshes

Neural Template: Topology-aware Reconstruction and Disentang...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hui, Ka-Hei Li, Ruihui Hu, Jingyu Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China Hunan Univ Changsha Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Beyond previous works, we learn a topology-aware neural template specific to each input then deform the template to reconstruct a detailed mesh while preserving the learned topology. One key insight is to de couple the complex mesh reconstruction into two sub-tasks: topology formulation and shape deformation. Thanks to the decoupling, DT-Net implicitly learns a disentangled representation for the topology and shape in the latent space. Hence, it can enable novel disentangled controls for supporting various shape generation applications, e.g., remix the topologies of 3D objects, that are not achievable by previous reconstruction works. Extensive experimental results demonstrate that our method' is able to produce high quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.

关键词： Three-dimensional displays Codes Shape Aerospace electronics reconstruction algorithms Topology Pattern recognition

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FvOR: Robust Joint Shape and Pose Optimization for Few-view Object reconstruction

FvOR: Robust Joint Shape and Pose Optimization for Few-view ...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang, Zhenpei Ren, Zhile Bautista, Miguel Angel Zhang, Zaiwei Shan, Qi Huang, Qixing Univ Texas Austin Austin TX 78712 USA Apple Cupertino CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. State-of-the-art approaches typically assume accurate camera poses as input, which could be difficult to obtain in realistic settings. In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses. The core of our approach is a fast and robust multi-view reconstruction algorithm to jointly refine 3D geometry and camera pose estimation using learnable neural network modules. We provide a thorough benchmark of state-of-the-art approaches for this problem on ShapeNet. Our approach achieves best-in-class results. It is also two orders of magnitude faster than the recent optimization-based approach IDR[67].

关键词： Solid modeling Computer vision Three-dimensional displays Shape Computational modeling Pose estimation reconstruction algorithms

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Shape reconstruction for Flexible Robot Using FBG Sensor

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第3期28卷 44-51页

作者： Jiang, Qi Wang, Feiwen Zhang, Yingxuan Shandong Univ Control Sci & Engn Dept Jinan Shandong Peoples R China

Flexible robots have shown great potential in medical applications. Highly accurate shape perception methods for flexible robots can effectively enhance their operability in complex environments. In this paper, an improved 3D shape reconstruction algorithm based on the Frenet-Serret formulas is proposed to reconstruct the robot curve. The complex 3D curve shape can be reconstructed by segmenting and stitching the curvature and torsion information collected by fiber Bragg grating sensor. The calibration experiments prove the reconstruction accuracy and calculation speed of the algorithm. The shape sensor for the flexible robot is fabricated, 2D and 3D shape sensing experiments are conducted. The average Euclidean errors of the reconstructed curve endpoints are 1.120 mm and 1.358 mm, and the average Frey interval distances are 1.922 mm and 2.533 mm, respectively. The experimental results showed that the designed shape sensor has high accuracy to meet the actual sensing requirements.

关键词： Surface reconstruction Optical fiber sensors Three-dimensional displays Accuracy reconstruction algorithms Robot sensing systems Fiber gratings Sensors Strain measurement Flexible structures

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Nonlinear reconstruction of coded spectral X-ray CT based on material decomposition

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OPTICS EXPRESS 2021年第13期29卷 19319-19339页

作者： Zhang, Tong Zhao, Shengjie Ma, Xu Cuadros, Angela P. Zhao, Qile Arce, Gonzalo R. Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Beijing Inst Technol Sch Opt & Photon Minist Educ China Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

Coded spectral X-ray computed tomography (CT) based on K-edge filtered illumination is a cost-effective approach to acquire both 3-dimensional structure of objects and their material composition. This approach allows sets of incomplete rays from sparse views or sparse rays with both spatial and spectral encoding to effectively reduce the inspection duration or radiation dose, which is of significance in biological imaging and medical diagnostics. However, reconstruction of spectral CT images from compressed measurements is a nonlinear and ill-posed problem. This paper proposes a material-decomposition-based approach to directly solve the reconstruction problem, without estimating the energy-binned sinograms. This approach assumes that the linear attenuation coefficient map of objects can be decomposed into a few basis materials that are separable in the spectral and space domains. The nonlinear problem is then converted to the reconstruction of the mass density maps of the basis materials. The dimensionality of the optimization variables is thus effectively reduced to overcome the ill-posedness. An alternating minimization scheme is used to solve the reconstruction with regularizations of weighted nuclear norm and total variation. Compared to the state-of-the-art reconstruction method for coded spectral CT, the proposed method can significantly improve the reconstruction quality. It is also capable of reconstructing the spectral CT images at two additional energy bins from the same set of measurements, thus providing more spectral information of the object. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Attenuation coefficient Biological imaging Image quality reconstruction algorithms Spectral imaging X ray computed tomography

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3D reconstruction of the specular surface using an iterative stereoscopic deflectometry method

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OPTICS EXPRESS 2021年第9期29卷 12867-12879页

作者： Han, Hao Wu, Shiqian Song, Zhan Gu, Feifei Zhao, Juan Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Wuhan Univ Sci & Technol Inst Robot & Intelligent Syst Wuhan 430081 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

Phase measuring deflectometry (PMD) is an effective technique for three-dimensional measurement of specular surfaces. However, the ambiguity of monoscopic PMD and the time-consuming searching process of stereoscopic PMD are challenges for specular surface reconstruction. To solve it, we propose an iterative reconstruction algorithm for the stereoscopic phase measuring deflectometry system free of the time-consuming searching process for each pixel. An arbitrary seed point on the specular surface is accurately obtained via a coarse-to-fine optimization means without any other expensive and complicate auxiliaries. Then, a plane with the height of seed point is set as the initial surface form for the iteration, in which the pinhole model is used to find the linear relation to update the surface form. The converging height is the output as the final result. Simulations and experiments verify the feasibility and efficiency of our proposed method based on the stereoscopic phase measuring deflectometry system. The accuracy and robustness are comprehensively evaluated as well. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Freeform surfaces Image reconstruction Laser sensors reconstruction algorithms Three dimensional measurement Three dimensional reconstruction

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Neural 3D Scene reconstruction with the Manhattan-world Assumption

Neural 3D Scene Reconstruction with the Manhattan-world Assu...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Guo, Haoyu Peng, Sida Lin, Haotong Wang, Qianqian Zhang, Guofeng Bao, Hujun Zhou, Xiaowei Zhejiang Univ Hangzhou Peoples R China Cornell Univ Ithaca NY 14853 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes. An approach to solving this issue is to incorporate planer constraints into the depth map estimation in multiview stereo-based methods, but the per-view plane estimation and depth optimization lack both efficiency and multiview consistency. In this work, we show that the planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. Specifically, we use an MLP network to represent the signed distance function as the scene geometry. Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network. To resolve the inaccurate segmentation, we encode the semantics of 3D points with another MLP and design a novel loss that jointly optimizes the scene geometry and semantics in 3D space. Experiments on ScanNet and 7-Scenes datasets show that the proposed method outperforms previous methods by a large margin on 3D reconstruction quality. The code and supplementary materials are available at https:// zju3dv. ***/manhattan_sdf.

关键词： Geometry Three-dimensional displays Semantics Estimation reconstruction algorithms Robustness Pattern recognition

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Approaching Sub-Nyquist Boundary: Optimized Compressed Spectrum Sensing Based on Multicoset Sampler for Multiband Signal

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 4225-4238页

作者： Song, Zihang Yang, Jian Zhang, Han Gao, Yue Univ Surrey Inst Commun Syst Guildford GU2 7XH Surrey England Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

Compressed spectrum sensing naturally pursues the use of fewer sampling resources to achieve spectrum support reconstruction and signal recovery. The theoretical lower boundary of averaging sampling rate to recover the multiband signal has been proved to be twice the Landau rate. However, it is still unreachable in practice. Based on the multicoset sampling architecture, this paper analyzes the influencing factors on perfect spectrum reconstruction from three aspects: data model, sampling pattern and greedy reconstruction algorithms, for which practical and feasible optimization schemes are proposed. To reduce redundant reconstruction for the multiple measurement vectors (MMV) signal model, a block MMV model is proposed to improve the accuracy in the spectrum support set reconstruction process. A sampling pattern selection algorithm is proposed to ensure a higher success rate of the spectrum support reconstruction to optimize the sensing matrix. We also deduced the representation of the signal and noise energy in the reconstructed spectrum based on the mathematical model of compressed sensing. We thus proposed a non-orthogonal double-threshold matching pursuit algorithm to avoid a high false-alarm rate due to the manually set converging conditions for matching pursuit algorithms. Numerical experiments on real-world wideband signals are carried out to demonstrate the feasibility and advantages of the proposed approaches. With integrated optimization, the required sampling density to ensure perfect reconstruction is approaching the sub-Nyquist sampling boundary.

关键词： Matching pursuit algorithms Sensors Mathematical models Signal processing algorithms Delays reconstruction algorithms Wideband Compressed spectrum sensing multiple measurement vectors matching pursuit algorithms multicoset sampling

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