scholarly journals Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization

2022 ◽  
Vol 14 (2) ◽  
pp. 288
Author(s):  
Yangyang Wang ◽  
Zhiming He ◽  
Xu Zhan ◽  
Yuanhua Fu ◽  
Liming Zhou
Keyword(s):  
Image Quality ◽  
Spatial Information ◽  
Computational Cost ◽  
Three Dimensional ◽  
Sar Image ◽  
Reconstruction Algorithms ◽  
3D Images ◽  
Sar Imaging ◽  
Regularization Techniques ◽  
Observation Matrix

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

scholarly journals 3D Object Shape Reconstruction from Underwater Multibeam Data and Over Ground Lidar Scanning

2018 ◽  
Vol 25 (2) ◽  
pp. 47-56 ◽  
Author(s):  
Marek Kulawiak ◽  
Zbigniew Łubniewski
Keyword(s):  
Laser Scanning ◽  
Spatial Information ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Measurement Data ◽  
Point Clouds ◽  
Shape Reconstruction ◽  
Reconstruction Algorithms ◽  
Processing Scheme ◽  
3D Shape Reconstruction

Abstract The technologies of sonar and laser scanning are an efficient and widely used source of spatial information with regards to underwater and over ground environment respectively. The measurement data are usually available in the form of groups of separate points located irregularly in three-dimensional space, known as point clouds. This data model has known disadvantages, therefore in many applications a different form of representation, i.e. 3D surfaces composed of edges and facets, is preferred with respect to the terrain or seabed surface relief as well as various objects shape. In the paper, the authors propose a new approach to 3D shape reconstruction from both multibeam and LiDAR measurements. It is based on a multiple-step and to some extent adaptive process, in which the chosen set and sequence of particular stages may depend on a current type and characteristic features of the processed data. The processing scheme includes: 1) pre-processing which may include noise reduction, rasterization and pre-classification, 2) detection and separation of objects for dedicated processing (e.g. steep walls, masts), and 3) surface reconstruction in 3D by point cloud triangulation and with the aid of several dedicated procedures. The benefits of using the proposed methods, including algorithms for detecting various features and improving the regularity of the data structure, are presented and discussed. Several different shape reconstruction algorithms were tested in combination with the proposed data processing methods and the strengths and weaknesses of each algorithm were highlighted.

scholarly journals Three-Dimensional Image Quality Evaluation and Optimization Based on Convolutional Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1041-1049
Author(s):  
Xiujuan Luo
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
3D Imaging ◽  
Quality Evaluation ◽  
Three Dimensional ◽  
3D Image ◽  
3D Images ◽  
Image Quality Evaluation ◽  
Global And Local

Currently, three-dimensional (3D) imaging has been successfully applied in medical health, movie viewing, games, and military. To make 3D images more pleasant to the eyes, the accurate judgement of image quality becomes the key step in content preparation, compression, and transmission in 3D imaging. However, there is not yet a satisfactory evaluation method that objectively assesses the quality of 3D images. To solve the problem, this paper explores the evaluation and optimization of 3D image quality based on convolutional neural network (CNN). Specifically, a 3D image quality evaluation model was constructed, and a 3D image quality evaluation algorithm was proposed based on global and local features. Next, the authors expounded on the preprocessing steps of salient regions in images, depicted the fusion process between global and local quality evaluations, and provided the way to process 3D image samples and acquire contrast-distorted images. The proposed algorithm was proved effective through experiments.

scholarly journals A Review on Real-Time 3D Ultrasound Imaging Technology

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Qinghua Huang ◽  
Zhaozheng Zeng
Keyword(s):  
Real Time ◽  
Ultrasound Imaging ◽  
Spatial Information ◽  
Imaging System ◽  
Three Dimensional ◽  
Intraoperative Ultrasound ◽  
3D Ultrasound ◽  
Interactive System ◽  
Reconstruction Algorithms ◽  
Advantages And Disadvantages

Real-time three-dimensional (3D) ultrasound (US) has attracted much more attention in medical researches because it provides interactive feedback to help clinicians acquire high-quality images as well as timely spatial information of the scanned area and hence is necessary in intraoperative ultrasound examinations. Plenty of publications have been declared to complete the real-time or near real-time visualization of 3D ultrasound using volumetric probes or the routinely used two-dimensional (2D) probes. So far, a review on how to design an interactive system with appropriate processing algorithms remains missing, resulting in the lack of systematic understanding of the relevant technology. In this article, previous and the latest work on designing a real-time or near real-time 3D ultrasound imaging system are reviewed. Specifically, the data acquisition techniques, reconstruction algorithms, volume rendering methods, and clinical applications are presented. Moreover, the advantages and disadvantages of state-of-the-art approaches are discussed in detail.

Measurements in 3D images

1991 ◽  
Vol 49 ◽  
pp. 408-409
Author(s):  
John C. Russ
Keyword(s):  
Three Dimensional ◽  
Image Plane ◽  
X Rays ◽  
Traditional Use ◽  
3D Images ◽  
Confocal Scanning Laser Microscope ◽  
Hard Materials ◽  
Depth Direction ◽  
Confocal Scanning ◽  
Confocal Scanning Laser

Three-dimensional (3D) images consisting of arrays of voxels can now be routinely obtained from several different types of microscopes. These include both the transmission and emission modes of the confocal scanning laser microscope (but not its most common reflection mode), the secondary ion mass spectrometer, and computed tomography using electrons, X-rays or other signals. Compared to the traditional use of serial sectioning (which includes sequential polishing of hard materials), these newer techniques eliminate difficulties of alignment of slices, and maintain uniform resolution in the depth direction. However, the resolution in the z-direction may be different from that within each image plane, which makes the voxels non-cubic and creates some difficulties for subsequent analysis.

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

scholarly journals Do 3D Face Images Capture Cues of Strength, Weight, and Height Better than 2D Face Images do?

2021 ◽  
Vol 7 (3) ◽  
pp. 209-219
Author(s):  
Iris J Holzleitner ◽  
Alex L Jones ◽  
Kieran J O’Shea ◽  
Rachel Cassar ◽  
Vanessa Fasolt ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Physical Characteristics ◽  
Two Dimensional ◽  
3D Images ◽  
3D Face ◽  
Face Images ◽  
Similar Accuracy ◽  
2D And 3D ◽  
2D Images ◽  
Better Than

Abstract Objectives A large literature exists investigating the extent to which physical characteristics (e.g., strength, weight, and height) can be accurately assessed from face images. While most of these studies have employed two-dimensional (2D) face images as stimuli, some recent studies have used three-dimensional (3D) face images because they may contain cues not visible in 2D face images. As equipment required for 3D face images is considerably more expensive than that required for 2D face images, we here investigated how perceptual ratings of physical characteristics from 2D and 3D face images compare. Methods We tested whether 3D face images capture cues of strength, weight, and height better than 2D face images do by directly comparing the accuracy of strength, weight, and height ratings of 182 2D and 3D face images taken simultaneously. Strength, height and weight were rated by 66, 59 and 52 raters respectively, who viewed both 2D and 3D images. Results In line with previous studies, we found that weight and height can be judged somewhat accurately from faces; contrary to previous research, we found that people were relatively inaccurate at assessing strength. We found no evidence that physical characteristics could be judged more accurately from 3D than 2D images. Conclusion Our results suggest physical characteristics are perceived with similar accuracy from 2D and 3D face images. They also suggest that the substantial costs associated with collecting 3D face scans may not be justified for research on the accuracy of facial judgments of physical characteristics.

scholarly journals The ICON Single-Column Mode

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 906
Author(s):  
Ivan Bašták Ďurán ◽  
Martin Köhler ◽  
Astrid Eichhorn-Müller ◽  
Vera Maurer ◽  
Juerg Schmidli ◽  
...  
Keyword(s):  
Data Storage ◽  
Model Development ◽  
Computational Cost ◽  
Three Dimensional ◽  
Shallow Convection ◽  
Modeling Framework ◽  
Additional Tool ◽  
Eddy Simulation ◽  
Single Column ◽  
Stratocumulus Clouds

The single-column mode (SCM) of the ICON (ICOsahedral Nonhydrostatic) modeling framework is presented. The primary purpose of the ICON SCM is to use it as a tool for research, model evaluation and development. Thanks to the simplified geometry of the ICON SCM, various aspects of the ICON model, in particular the model physics, can be studied in a well-controlled environment. Additionally, the ICON SCM has a reduced computational cost and a low data storage demand. The ICON SCM can be utilized for idealized cases—several well-established cases are already included—or for semi-realistic cases based on analyses or model forecasts. As the case setup is defined by a single NetCDF file, new cases can be prepared easily by the modification of this file. We demonstrate the usage of the ICON SCM for different idealized cases such as shallow convection, stratocumulus clouds, and radiative transfer. Additionally, the ICON SCM is tested for a semi-realistic case together with an equivalent three-dimensional setup and the large eddy simulation mode of ICON. Such consistent comparisons across the hierarchy of ICON configurations are very helpful for model development. The ICON SCM will be implemented into the operational ICON model and will serve as an additional tool for advancing the development of the ICON model.

scholarly journals Retinal vessel segmentation with constrained-based nonnegative matrix factorization and 3D modified attention U-Net

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Yu ◽  
Hongqing Zhu
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Computational Cost ◽  
Three Dimensional ◽  
Nonnegative Matrix ◽  
Retinal Vessel ◽  
Classification Problem ◽  
Image Resolution ◽  
Retinal Vessels ◽  
Segmentation Task

AbstractDue to the complex morphology and characteristic of retinal vessels, it remains challenging for most of the existing algorithms to accurately detect them. This paper proposes a supervised retinal vessels extraction scheme using constrained-based nonnegative matrix factorization (NMF) and three dimensional (3D) modified attention U-Net architecture. The proposed method detects the retinal vessels by three major steps. First, we perform Gaussian filter and gamma correction on the green channel of retinal images to suppress background noise and adjust the contrast of images. Then, the study develops a new within-class and between-class constrained NMF algorithm to extract neighborhood feature information of every pixel and reduce feature data dimension. By using these constraints, the method can effectively gather similar features within-class and discriminate features between-class to improve feature description ability for each pixel. Next, this study formulates segmentation task as a classification problem and solves it with a more contributing 3D modified attention U-Net as a two-label classifier for reducing computational cost. This proposed network contains an upsampling to raise image resolution before encoding and revert image to its original size with a downsampling after three max-pooling layers. Besides, the attention gate (AG) set in these layers contributes to more accurate segmentation by maintaining details while suppressing noises. Finally, the experimental results on three publicly available datasets DRIVE, STARE, and HRF demonstrate better performance than most existing methods.

scholarly journals Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

Vibration ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 49-63
Author(s):  
Waad Subber ◽  
Sayan Ghosh ◽  
Piyush Pandita ◽  
Yiming Zhang ◽  
Liping Wang
Keyword(s):  
Dynamical Systems ◽  
Computational Cost ◽  
Three Dimensional ◽  
Region Of Interest ◽  
System Response ◽  
Computational Domain ◽  
User Interest ◽  
Numerical Resolution ◽  
Multi Scale ◽  
Operation Conditions

Industrial dynamical systems often exhibit multi-scale responses due to material heterogeneity and complex operation conditions. The smallest length-scale of the systems dynamics controls the numerical resolution required to resolve the embedded physics. In practice however, high numerical resolution is only required in a confined region of the domain where fast dynamics or localized material variability is exhibited, whereas a coarser discretization can be sufficient in the rest majority of the domain. Partitioning the complex dynamical system into smaller easier-to-solve problems based on the localized dynamics and material variability can reduce the overall computational cost. The region of interest can be specified based on the localized features of the solution, user interest, and correlation length of the material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update the prior knowledge of the localized region of interest using measurements of the system response. Once, the region of interest is identified, the localized uncertainty is propagate forward through the computational domain. We demonstrate our framework using numerical experiments on a three-dimensional elastodynamic problem.

scholarly journals Virtual Archaeology of Death and Burial: A Procedure for Integrating 3D Visualization and Analysis in Archaeothanatology

2021 ◽  
Vol 7 (1) ◽  
pp. 540-555
Author(s):  
Hayley L. Mickleburgh ◽  
Liv Nilsson Stutz ◽  
Harry Fokkens
Keyword(s):  
Spatial Information ◽  
3D Visualization ◽  
Rapid Development ◽  
Three Dimensional ◽  
3D Models ◽  
The Body ◽  
Test Case ◽  
Archaeology Of Death ◽  
Human Burials ◽  
3D Information

Abstract The reconstruction of past mortuary rituals and practices increasingly incorporates analysis of the taphonomic history of the grave and buried body, using the framework provided by archaeothanatology. Archaeothanatological analysis relies on interpretation of the three-dimensional (3D) relationship of bones within the grave and traditionally depends on elaborate written descriptions and two-dimensional (2D) images of the remains during excavation to capture this spatial information. With the rapid development of inexpensive 3D tools, digital replicas (3D models) are now commonly available to preserve 3D information on human burials during excavation. A procedure developed using a test case to enhance archaeothanatological analysis and improve post-excavation analysis of human burials is described. Beyond preservation of static spatial information, 3D visualization techniques can be used in archaeothanatology to reconstruct the spatial displacement of bones over time, from deposition of the body to excavation of the skeletonized remains. The purpose of the procedure is to produce 3D simulations to visualize and test archaeothanatological hypotheses, thereby augmenting traditional archaeothanatological analysis. We illustrate our approach with the reconstruction of mortuary practices and burial taphonomy of a Bell Beaker burial from the site of Oostwoud-Tuithoorn, West-Frisia, the Netherlands. This case study was selected as the test case because of its relatively complete context information. The test case shows the potential for application of the procedure to older 2D field documentation, even when the amount and detail of documentation is less than ideal.

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