Robust Reconstruction Algorithm Research Articles

FACE: Feature-preserving CAD model surface reconstruction

Feature lines play a pivotal role in the reconstruction of CAD models. Currently, there is a lack of a robust explicit reconstruction algorithm capable of achieving sharp feature reconstruction in point clouds with noise and non-uniformity. In this paper, we propose a feature-preserving CAD model surface reconstruction algorithm, named FACE. The algorithm initiates with preprocessing the point cloud through denoising and resampling steps, resulting in a high-quality point cloud that is devoid of noise and uniformly distributed. Then, it employs discrete optimal transport to detect feature regions and subsequently generates dense points along potential feature lines to enhance features. Finally, the advancing-front surface reconstruction method, based on normal vector directions, is applied to reconstruct the enhanced point cloud. Extensive experiments demonstrate that, for contaminated point clouds, this algorithm excels not only in reconstructing straight edges and corner points but also in handling curved edges and surfaces, surpassing existing methods.

Robust hyperspectral reconstruction via a multi-channel clustering compressive sensing approach

Wavelength-coded spectral imaging represents a fusion of spectral imaging and compressed sensing, offering advantages such as reduced storage requirements and straightforward miniaturization. This approach employs optical filters for hyperspectral reconstruction. However, issues like insufficient energy and excessive noise arise in low-light detection, leading to a notable degradation of image reconstruction quality. This paper presents a robust segmented reconstruction algorithm named MCC-WSCI (Multi-Channel Clustering-based Wavelength-Coded Spectral Imaging), designed to enhance the method's tolerance to noise. This algorithm can accurately classify and process compressed images obtained by wavelength-coded spectral imaging systems, thus improving reconstruction quality significantly. Numerical simulations and experiments in low-light scenarios are carried out to verify the proposed method. Results show that the MCC-WSCI method is robust to different noise and sampling rates and outperforms other state-of-the-art compressed sensing reconstruction methods in terms of reconstructed spatial resolution and spectral resolution. The proposed method provides effective experimental robustness to wavelength-coded spectral imaging with a natural algorithmic extension, paving the way for its application in remote sensing.

EventLFM: event camera integrated Fourier light field microscopy for ultrafast 3D imaging

Ultrafast 3D imaging is indispensable for visualizing complex and dynamic biological processes. Conventional scanning-based techniques necessitate an inherent trade-off between acquisition speed and space-bandwidth product (SBP). Emerging single-shot 3D wide-field techniques offer a promising alternative but are bottlenecked by the synchronous readout constraints of conventional CMOS systems, thus restricting data throughput to maintain high SBP at limited frame rates. To address this, we introduce EventLFM, a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy (LFM), a state-of-the-art single-shot 3D wide-field imaging technique. The event camera operates on a novel asynchronous readout architecture, thereby bypassing the frame rate limitations inherent to conventional CMOS systems. We further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by EventLFM. Experimental results demonstrate that EventLFM can robustly reconstruct fast-moving and rapidly blinking 3D fluorescent samples at kHz frame rates. Furthermore, we highlight EventLFM’s capability for imaging of blinking neuronal signals in scattering mouse brain tissues and 3D tracking of GFP-labeled neurons in freely moving C. elegans. We believe that the combined ultrafast speed and large 3D SBP offered by EventLFM may open up new possibilities across many biomedical applications.

Robust residual-guided iterative reconstruction for sparse-view CT in small animal imaging

Objective. We introduce a robust image reconstruction algorithm named residual-guided Golub–Kahan iterative reconstruction technique (RGIRT) designed for sparse-view computed tomography (CT), which aims at high-fidelity image reconstruction from a limited number of projection views. Approach. RGIRT utilizes an inner-outer dual iteration framework, with a flexible least square QR (FLSQR) algorithm implemented in the inner iteration and a restarted iterative scheme applied in the outer iteration. The inner FLSQR employs a flexible Golub–Kahan bidiagonalization method to reduce the size of the inverse problem, and a weighted generalized cross-validation method to adaptively estimate the regularization hyper-parameter. The inner iteration efficiently yields the intermediate reconstruction result, while the outer iteration minimizes the residual and refines the solution by using the result obtained from the inner iteration. Main results. The reconstruction performance of RGIRT is evaluated and compared to other reference methods (FBPConvNet, SART-TV, and FLSQR) using projection data from both numerical phantoms and real experimental Micro-CT data. The experimental findings, from testing various numbers of projection views and different noise levels, underscore the robustness of RGIRT. Meanwhile, theoretical analysis confirms the convergence of residual for our approach. Significance. We propose a robust iterative reconstruction algorithm for x-ray CT scans with sparse views, thereby shortening scanning time and mitigating excessive ionizing radiation exposure to small animals.

Piecewise circular interface construction using height functions

AbstractA piecewise circular interface construction (PCIC) method is described, where height functions based curvature estimates are directly utilised for accurate interface reconstruction under the framework of volume of fluid method. The present work is an attempt to develop a robust and accurate higher order interface reconstruction algorithm that is capable of accurate simulation of surface tension dominated flows. The proposed hybrid method (H‐PCIC) is thus able to take advantage of merits of both PCIC and HF methods, achieving at least second order convergence with respect to both interface reconstruction and curvature computation. This is in addition to the significantly superior quality of the reconstructed interface with respect to PLIC methods. This seamless blending of the HF and PCIC quantities is enabled by c0‐correction procedures applied to base PLIC and initial PCIC steps. More recent variants of the height function method with variable stencil size are used for calculation of radius of curvature. The capability of this proposed method towards simulation of flow problems within a well‐balanced two‐phase solver is established with help of multiple complex two‐phase flow problems. This validation exercise also demonstrates the capability of PCIC class of methods towards solutions of two‐phase flows with intricate physics.

Unified shape and appearance reconstruction with joint camera parameter refinement

In this paper, we present an inverse rendering method for the simple reconstruction of shape and appearance of real-world objects from only roughly calibrated RGB images captured under collocated point light illumination. To this end, we gradually reconstruct the lower-frequency geometry information using automatically generated occupancy mask images based on a visual hull initialization of the mesh, to infer the object topology, and a smoothness-preconditioned optimization. By combining this geometry estimation with learning-based SVBRDF parameter inference as well as intrinsic and extrinsic camera parameter refinement in a joint and unified formulation, our novel method is able to reconstruct shape and an isotropic SVBRDF from fewer input images than previous methods. Unlike in other works, we also estimate normal maps as part of the SVBRDF to capture and represent higher-frequency geometric details in a compact way. Furthermore, by regularizing the appearance estimation with a GAN-based SVBRDF generator, we are able to meaningfully limit the solution space. In summary, this leads to a robust automatic reconstruction algorithm for shape and appearance. We evaluated our algorithm on synthetic as well as on real-world data and demonstrate that our method is able to reconstruct complex objects with high-fidelity reflection properties in a robust way, also in the presence of imperfect camera parameter data.

ELFPIE: an error-laxity Fourier ptychographic iterative engine

We present a simple but efficient and robust reconstruction algorithm for Fourier ptychographic microscopy, termed error-laxity Fourier ptychographic iterative engine (Elfpie), that is simultaneously robust to (1) noise signal (including Gaussian, Poisson, and salt & pepper noises), (2) problematic background illumination problem, (3) vignetting effects and (4) misaligning of LED positions, without the need of calibrating or recovering these system errors. In Elfpie, we embed the inverse problem of FPM under the framework of feature extraction/recovering and propose a new image gradient-based data fidelity cost function regularized by the global second-order total-variation regularization. The closed-form complex gradient for the cost function is derived and is back-propagated using the AdaBelief optimizer with an adaptive learning rate. The Elfpie was tested on both simulation and experimental data. In general, compared against SOTA methods, the Elfpie is robust to Gaussian noise with a 100 times larger noise, salt & pepper noise with 1000 times larger noise and Poisson noise with 10 times larger noise. The Elfpie is able to reconstruct high-fidelity samples under LED position misalignments up to 2 mm. It can also bypass the vignetting effect, for which all SOTA methods fail to reconstruct the sample pattern. The MATLAB code for ELFPIE is available on Github.

Robust sparse Bayesian learning based on the Bernoulli-Gaussian model of impulsive noise

Traditional sparse reconstruction methods suffer from performance degradation in the presence of impulsive noise. In this paper, we adopt the Bernoulli-Gaussian distribution to model the impulsive noise and introduce the hidden state variables to indicate the state of the noise. Based on the new hierarchical Bayesian model, a robust sparse reconstruction algorithm called Bernoulli-Gaussian robust sparse Bayesian learning (BG-RSBL) is developed through the variational Bayesian inference framework. Different from existing robust reconstruction methods, BG-RSBL does not make sparse assumptions about the impulses and outliers. The employment of the Bernoulli-Gaussian model enables BG-RSBL to adapt to environments with a wider range of impulse densities. Comparative analysis shows that the reconstruction performance can be improved by including the outliers in posterior inference with appropriate weightings. Simulations performed under both Bernoulli-Gaussian and symmetric α-stable distributed impulsive noise demonstrate the robust and outstanding reconstruction performance of the proposed algorithm.

Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation.

The efforts in compressive sensing (CS) literature can be divided into two groups: finding a measurement matrix that preserves the compressed information at its maximum level, and finding a robust reconstruction algorithm. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solutions are used to recover the signals. Using random matrices when handling large or multi-dimensional signals is cumbersome especially when it comes to iterative optimizations. Recent deep learning-based solutions increase reconstruction accuracy while speeding up recovery, but jointly learning the whole measurement matrix remains challenging. For this reason, state-of-the-art deep learning CS solutions such as convolutional compressive sensing network (CSNET) use block-wise CS schemes to facilitate learning. In this work, we introduce a separable multi-linear learning of the CS matrix by representing the measurement signal as the summation of the arbitrary number of tensors. As compared to block-wise CS, tensorial learning eases blocking artifacts and improves performance, especially at low measurement rates (MRs), such as [Formula: see text]. The software implementation of the proposed network is publicly shared at https://github.com/mehmetyamac/GTSNET.

A Graph-guided Hybrid Regularization Method For Bioluminescence Tomography

Background and objective: Bioluminescence tomography (BLT) is a powerful and sensitive imaging technique having great potential in preclinical application, such as tumor imaging, monitoring and therapy, etc. Regularization plays an important role in BLT reconstruction for considering the priori information to overcome the inherent ill-posedness of the inverse problem. Therefore, well-designed regularization term and sophisticated algorithm for solving the consequent optimization problem are key to improve the BLT quality.Methods: To balance the sparsity, smoothness and morphological characteristics of the bioluminescence targets, we constructed a novel Graph-Guided Hybrid Regularization (GGHR) method by combining graph-guided penalty term with L1 and L2 norm regularizer. To solve the corresponding minimization problem with hybrid penalties, the dual decomposition and Nesterov’s smoothing technique were adopted to decouple and transform the non-separable and non-smooth graph-guided penalty term into a differential smooth approximation form, which was solved by the fast iterative shrinkage thresholding algorithm.Results: The performance of the proposed GGHR method was verified and evaluated through a series of simulation, phantom and in vivo experiments. The comparison results demonstrated that the GGHR method outperformed current mainstream reconstruction algorithms in spatial localization, morphology recovery and in vivo practicality.Conclusions: The proposed GGHR method is a robust and practicality reconstruction algorithm for further highlighting the positive effect of hybrid regularization on BLT applications.

A cell-centered discontinuous Galerkin multi-material arbitrary Lagrangian-Eulerian method in axisymmetric geometry

In this paper, a cell-centered discontinuous Galerkin (DG) multi-material arbitrary Lagrangian-Eulerian (MMALE) method is developed for compressible fluid dynamics in axisymmetric geometry. The MMALE method utilizes moment-of-fluid (MOF) interface reconstruction technology to simulate multi-materials of immiscible fluids. It is an explicit time-marching Lagrangian plus remapping type. In the Lagrangian stage, a volume-weighted scheme of updated cell-centered discontinuous Galerkin Lagrangian method is applied to discretize the hydrodynamic equations in pure cells, and Tipton's pressure relaxation closure model is used in mixed cells. A robust MOF interface reconstruction algorithm is used to provide the information on the material interfaces for remapping. In the rezoning stage, Knupp's algorithm is used for mesh smoothing. In the remapping stage, an efficient multi-material cell-intersection-based remapping method is proposed. It can be divided into four stages: polynomial reconstruction, polygon intersection, integration, and detection of problematic cells and limiting. Polygon intersection is based on the “clipping and projecting” algorithm, and detection of problematic cells depends on a troubled cell marker, and a posteriori multi-dimensional optimal order detection (MOOD) limiting strategy is used for limiting. Several numerical tests are given to demonstrate the robustness and accuracy of the method.

Robust frame-reduced structured illumination microscopy with accelerated correlation-enabled parameter estimation

Structured illumination microscopy (SIM), with the advantages of full-field imaging and low photo-damage, is one of the most well-established fluorescence super-resolution microscopy techniques that raised great interest in biological sciences. However, conventional SIM techniques generally require at least nine images for image reconstruction, and the quality of super-resolution significantly depends on high-accuracy illumination parameter estimation, which is usually computationally intense and time-consuming. To address these issues, we propose a robust seven-frame SIM reconstruction algorithm with accelerated correlation-enabled parameter estimation. First, a modulation-assigned spatial filter is employed to remove unreliable backgrounds associated with low signal-to-noise ratios. Then, we propose a coarse-to-fine accelerated correlation algorithm to eliminate the redundant iterations of the traditional correlation-based scheme. The frame reduction is achieved by a specially designed phase-shifting strategy combined with pixel-wise fluorescence pre-calibration. We experimentally demonstrate that, compared with conventional iterative correlation-based methods, the proposed algorithm improves the computational efficiency by a factor of 4.5 while maintaining high accuracy illumination parameter estimation. Meanwhile, our method achieves high-quality super-resolution reconstruction even with a reduction in two raw images, which improves the efficiency of image acquisition and ensures the robustness toward complex experimental environments.

Highly accurate THz-CT including refraction effects

The principles of algebraic image reconstruction are applied to THz computed tomography (THz-CT) in order to account for refraction within the sample. Using the nominal sample geometry as a priori knowledge, a highly accurate and robust image reconstruction algorithm based on the physics of geometric optics is presented. The validity of the geometric forward model is verified by a numerical simulation of Maxwell's equations. Furthermore, the developed method is experimentally tested using measurements performed with a fast THz-CT system based on a THz time-domain spectrometer in transmission mode. Automated evaluations of the reconstructed sample cross sections showed an accuracy of <150 μm.

Real-time, multiplexed holographic tomography

Holographic tomography (HT) has become an established, label-free, quantitative measurement technique, which uses the refractive index (RI) as the contrasting agent for cell and tissue analysis. However, the three-dimensional RI imaging is performed at the cost of measurement time, by sequentially acquiring the projections of the measured object. In this paper a simple, single-exposure HT system is proposed. It allows to record a full set of projections multiplexed in a single hologram. The projections are generated by a microlens array and the system works with a robust tomographic reconstruction algorithm to provide satisfactory quality of the 3D RI distribution. The result is demonstrated with reconstructions of calibration objects and biological samples.

Adaptive Grouping Block Sparse Bayesian Learning Method for Accurate and Robust Reconstruction in Bioluminescence Tomography.

Bioluminescence tomography (BLT) is a promising modality that is designed to provide non-invasive quantitative three-dimensional information regarding the tumor distribution in living animals. However, BLT suffers from inferior reconstructions due to its ill-posedness. This study aims to improve the reconstruction performance of BLT. We propose an adaptive grouping block sparse Bayesian learning (AGBSBL) method, which incorporates the sparsity prior, correlation of neighboring mesh nodes, and anatomical structure prior to balance the sparsity and morphology in BLT. Specifically, an adaptive grouping prior model is proposed to adjust the grouping according to the intensity of the mesh nodes during the optimization process. Numerical simulations and in vivo experiments demonstrate that AGBSBL yields a high position and morphology recovery accuracy, stability, and practicality. The proposed method is a robust and effective reconstruction algorithm for BLT. Moreover, the proposed adaptive grouping strategy can further increase the practicality of BLT in biomedical applications.

Target Adaptive Differential Iterative Reconstruction (TADI): A Robust Algorithm for Real-Time Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a noninvasive functional diagnostic technique that has been successfully applied to human lung and brain. EIT reconstruction is an ill-conditioned problem: the regularization parameters establish a trade-off between acceptable data fitting and acceptable solution stability. This trade-off is necessary to select the optimal regularization parameters for each data frame to continuously obtain high-quality images in real-time monitoring. However, using the objective regularization parameter selection method before each image reconstruction may reduce the reconstruction efficiency, and using heuristic selection for all images may cause significant quality degradation, thereby creating challenges in long-term clinical EIT monitoring. This study explores a robust EIT target adaptive differential iterative reconstruction algorithm that does not require the advance selection of the optimal regularization parameter to facilitate the clinical application of EIT long-term bedside monitoring. Specifically, second-order Taylor approximation expansion and Euler–Lagrange theorem were used to derive the conductivity differential iteration relationship. Furthermore, the reconstruction quality and generalization ability of the proposed algorithm were validated based on comparisons with the L-curve and the generalized cross-validation parameter selection methods through simulations, phantom experiments, and human lung recruitment data. Compared with the L-curve and generalized cross-validation methods, the TADI algorithm reduces the position error by 33.07% and 47.17%, the ringing by 26.49% and 32.69%, and the shape deformation by 18.34% and 19.40% on average, respectively. The results revealed that the TADI algorithm could achieve a significant improvement over the existing state-of-the-art methods in terms of stability, consistency, and efficiency.

A sensitive mNeonGreen reporter system to measure transcriptional dynamics in Drosophila development

The gene regulatory network governing anterior–posterior axis formation in Drosophila is a well-established paradigm to study transcription in developmental biology. The rapid temporal dynamics of gene expression during early stages of development, however, are difficult to track with standard techniques. We optimized the bright and fast-maturing fluorescent protein mNeonGreen as a real-time, quantitative reporter of enhancer expression. We derive enhancer activity from the reporter fluorescence dynamics with high spatial and temporal resolution, using a robust reconstruction algorithm. By comparing our results with data obtained with the established MS2-MCP system, we demonstrate the higher detection sensitivity of our reporter. We used the reporter to quantify the activity of variants of a simple synthetic enhancer, and observe increased activity upon reduction of enhancer–promoter distance or addition of binding sites for the pioneer transcription factor Zelda. Our reporter system constitutes a powerful tool to study spatio-temporal gene expression dynamics in live embryos.

A low-cost, practical acquisition and rendering pipeline for real-time free-viewpoint video communication

We present a semiautomatic real-time pipeline for capturing and rendering free-viewpoint video using passive stereo matching. The pipeline is simple and achieves agreeable quality in real time on a system of commodity web cameras and a single desktop computer. We suggest an automatic algorithm to compute a constrained search space for an efficient and robust hierarchical stereo reconstruction algorithm. Due to our fast reconstruction times, we can eliminate the need for an expensive global surface reconstruction with a combination of high coverage and aggressive filtering. Finally, we employ a novel color weighting scheme that generates credible new viewpoints without noticeable seams, while keeping the computational complexity low. The simplicity and low cost of the system make it an accessible and more practical alternative for many applications compared to previous methods.

Moment-of-fluid analytic reconstruction on 3D rectangular hexahedrons

The moment-of-fluid method (MOF) is a second-order accurate interface reconstruction method which can be seen as an extension of the volume-of-fluid method with piecewise linear interface construction (VOF-PLIC). MOF involves a computationally intensive minimization problem that needs to be solved on every cell containing several materials. We propose a new fast and robust reconstruction algorithm to tackle this problem on rectangular hexahedral cells. Our approach uses explicit analytic formulas of the objective function that does not use any geometric computations such as half-space–polyhedron intersections. The numerical results show that the proposed method is more robust and more than 200 times faster than the original approach. Additionally, we propose a faster reconstruction algorithm on convex polyhedral cells. All the methods presented in this article have been implemented and verified on the open-source code Notus.

Fast and robust reconstruction algorithm for fluorescence diffuse optical tomography assuming a cuboid target.

A fast algorithm for fluorescence diffuse optical tomography is proposed. The algorithm is robust against the choice of initial guesses. We estimate the position of a fluorescent target by assuming a cuboid (rectangular parallelepiped) for the fluorophore target. The proposed numerical algorithm is verified by a numerical experiment and an experiment with a meat phantom. The target position is reconstructed with a cuboid from measurements in the time domain. Moreover, the long-time behavior of the emission light is investigated making use of the analytical solution to the diffusion equation.