Imaging Problems Research Articles

Stable neural network-based traveltime tomography using hard-constrained measurements

Traveltime tomography, or travel time inversion, has been one of the primary seismological tools for decades and has been used to understand Earth's properties and dynamic processes. An accurate, preferably flexible, eikonal solver to compute the travel time field is at the heart of the inversion process. However, most conventional eikonal solvers suffer from first-order convergence errors and difficulties dealing with irregular computational grids. Physics-informed neural networks (PINNs) have been introduced to tackle these problems and have successfully addressed those challenges. Nevertheless, these approaches still suffer from slow convergence and unstable training dynamics due to the multi-term nature of the loss function. To improve this, we propose a new formulation for the isotropic eikonal equation, which imposes boundary conditions as hard constraints. We employ the theory of functional connections to the traveltime tomography problem, which allows for the use of a single loss term for training the PINN model. Its efficiency, stability, and flexibility in tackling various cases, including topography-dependent and 3D models, are attested through rigorous numerical tests, thus providing an efficient and stable PINN-based traveltime tomography.

GAN-Based High-Quality Face-Swapping Composite Network

Face swapping or face replacement is a challenging task that involves transferring a source face to a target face while maintaining the target’s facial motion and expression. Although many studies have made a lot of encouraging progress, we have noticed that most of the current solutions have the problem of blurred images, abnormal features, and unnatural pictures after face swapping. To solve these problems, in this paper, we proposed a composite face-swapping generation network, which includes a face extraction module and a feature fusion generation module. This model retains the original facial expression features, as well as the background and lighting of the image while performing face swapping, making the image more realistic and natural. Compared with other excellent models, our model is more robust in terms of face identity, posture verification, and image quality.

Development and modification Sobel edge detection in tuberculosis X-ray images

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.

A light-weight and generalizable deep learning model for the prediction of COVID-19 from chest X-ray images

The detection of coronavirus disease (COVID-19) using standard laboratory tests, such as reverse transcription polymerase chain reaction (RT-PCR), is time-consuming. Complex medical imaging problems are currently being solved using machine learning and deep learning techniques. Our proposed solution utilizes chest radiography imaging techniques, which have shown to be a faster alternative for detecting COVID-19. We present an efficient and lightweight deep learning architecture for identifying COVID-19 using chest X-ray images which achieve 99.81% accuracy in intra-database testing and 100% accuracy in cross-validation testing on a separate data set. The results demonstrate the potential of our proposed model as a reliable tool for COVID-19 diagnosis using chest X-ray images, which can have a significant impact on improving the efficiency of COVID-19 diagnosis and treatment.

A fast adaptive spatio-temporal fusion method to enhanced Fit-FC.

Space-time fusion is an economical and efficient way to solve "space-time contradiction". Among all kinds of space-time fusion methods, Fit-FC space-time fusion method based on weight Function is widely used. However, this method is based on the linear model to depict the phase change, but the phase change in the real scene is complicated, and the linear model is difficult to accurately capture the phase change, resulting in the spectral distortion of the fusion image. In addition, pixel-by-pixel scanning with moving Windows leads to inefficiency issues, limiting its use in large-scale and long-term tasks. To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. Secondly, an adaptive window selection Function is established to overcome the problem of manually setting parameters on different data sets, improve the convenience of the algorithm and robustness of the application on different data sets, and make the algorithm simpler and more efficient. Finally, the improved AL-FF algorithm is compared with other algorithms to verify the performance improvement. Compared with the current advanced Spatio-Temporal fusion methods, AL-FF algorithm has stronger detail capture ability and can generate more accurate fusion results. In addition, the computational efficiency is significantly improved, and the efficiency is increased by more than 20 times compared with the current mainstream method.

ERT image reconstruction using marker region segmentation method

Abstract Inspired by image region segmentation method, a marking region segmentation iterative method is proposed to reconstruct sparse binary images of electrical resistance tomography. The grayscale matrix of the iteration process is mapped to another linear space for segmentation processing, adding the watershed thresholding of marking regions. In the iteration process for estimating conductivity distribution, the target regions are separated to avoid excessive segmentation effects. By applying this method in conjunction with the Landweber iterative model to solve the inverse problem of resistivity tomography imaging, more accurate binary images can be obtained, and the method exhibits superior convergence properties in comparison to the Landweber algorithm. To verify the reconstruction effectiveness of LW-TDIS method, numerical simulations and static experiments are conducted for comparison with three other methods. The results demonstrate that the proposed method effectively reduces reconstruction artifacts, improves reconstruction quality, and achieves better reconstruction performance.

Stochastic Gauss-Newton method for estimating absorption and scattering in optical tomography with the Monte Carlo method for light transport.

Image reconstruction in optical tomography in the so-called transport regime, where the diffusion approximation is not valid, requires modeling of light transport using the radiative transfer equation. In this work, we approach this problem by utilizing the Monte Carlo method for light transport. In this work, we propose a methodology for absolute imaging of absorption and scattering in this regime utilizing a Monte Carlo method for light transport. The image reconstruction problem is formulated as a minimization problem that is solved using a stochastic Gauss-Newton method. In the construction of the Jacobian matrix for scattering, a perturbation approximation for Monte Carlo is utilized. The approach is evaluated with numerical simulations using an adaptive approach where the number of photon packets is adjusted during the iterations, and with different fixed numbers of photon packets. The simulations show that the Monte Carlo method for light transport can be utilized in the absolute imaging problem of optical tomography and that the absorption and scattering parameters can be estimated simultaneously with good accuracy.

Self-supervised learning for improved calibrationless radial MRI with NLINV-Net.

To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via nonlinear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k-space-based SSDU loss on the region of interest. NLINV-Net-based reconstructions were compared with conventional NLINV and PI-CS (parallel imaging + compressed sensing) reconstruction and the effect of the region-optimized virtual coils and the type of training loss was evaluated qualitatively. NLINV-Net-based reconstructions contain significantly less noise than the NLINV-based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir-based focused loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real-time imaging. For quantitative imaging, T1-maps reconstructed using NLINV-Net show similar quality as PI-CS reconstructions, but NLINV-Net does not require slice-specific tuning of the regularization parameter. NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.

Dostoevsky in Japanese Translations: The Problem of Textual Images

Dostoevsky in Japanese Translations: The Problem of Textual Images

Neural‐network‐based regularization methods for inverse problems in imaging

AbstractThis review provides an introduction to—and overview of—the current state of the art in neural‐network based regularization methods for inverse problems in imaging. It aims to introduce readers with a solid knowledge in applied mathematics and a basic understanding of neural networks to different concepts of applying neural networks for regularizing inverse problems in imaging. Distinguishing features of this review are, among others, an easily accessible introduction to learned generators and learned priors, in particular diffusion models, for inverse problems, and a section focusing explicitly on existing results in function space analysis of neural‐network‐based approaches in this context.

CvT-UNet: A weld pool segmentation method integrating a CNN and a transformer

In the acquisition of weld seam images, the presence of light and impurity interference often leads to weak edge problems in weld seam images, making the identification of weld seam defects challenging. In such scenarios, segmentation processing must be performed specifically at the fusion pool of the weld seam. In recent years, Transformers, which leverage self-attention mechanisms, have demonstrated superior sequence modeling capabilities compared to convolutional neural networks (CNNs). The Transformer excels at weighting different positions within a sequence, enabling effective handling of long-range dependencies. However, Transformers lack the ability of CNNs to extract local information. To address this limitation, this paper introduces a novel CvT-UNet semantic segmentation model that combines the strengths of both the convolutional neural network and the transformer. The model combines the global context information capabilities of the Transformer with the spatial information advantages provided by the convolutional neural network. The proposed model employs an encoder-decoder architecture by building a U-shaped network with designed CvT blocks. Depthwise separable convolutions are utilized to balance parameter quantities, and the skip-connection module is redesigned to enable the model to achieve more precise segmentation of weld seam fusion pools with fewer parameters. Experimental evaluations on diverse weld seam datasets under different environmental conditions demonstrate promising results. The model achieves average intersection over union (IoU) values of 93.75 %, 88.31 %, and 90.86 %, respectively. Specifically, on an automotive weld seam dataset, CvT-UNet outperforms the purely convolutional neural network structure UNet3+, with a 1.63 % increase in the mean IoU (MIOU) and a 1.39 % increase in the mean pixel accuracy (MPA). Compared to the TransUNet fusion model, CvT-UNet results in a 0.33 % increase in the MIOU and a 0.6 % increase in the MPA. Furthermore, compared to the state-of-the-art curve segmentation model LIOT, CvT-UNet achieves a 1.94 % increase in the MIOU and a 0.61 % increase in the MPA, demonstrating favorable segmentation performance.

Computed tomography of chemiluminescence using a data-driven sparse sensing framework

The aim of this work is to demonstrate the use of data-driven sparse sensing for the reconstruction of the three-dimensional chemiluminescence field of a flame from the two-dimensional line-of-sight integrated chemiluminescence signal. By leveraging the intrinsic low-dimensionality of physical phenomena, sparse sensing can be employed to reconstruct a signal from few samples. This makes it a good candidate to be employed in computed tomography of chemiluminescence, an imaging technique used to reconstruct the chemiluminescence field from chemiluminescence images. In the proposed methodology, the transforming basis used to project the system’s state in the low-dimensional manifold is computed using the proper orthogonal decomposition, which is a popular machine-learning technique for data compression. The methodology is demonstrated on a virtual experiment based on the data coming from the large eddy simulation of a jet flame in a vitiated coflow, where OH is employed as a surrogate of OH∗. This methodology is then compared to conventional techniques that are generally employed to solve tomographic problems. The results show that the proposed technique is able to reconstruct the chemiluminescence field using only one view, compared to the multiple views required by conventional methods. Moreover, our techniques is also capable of predicting the distribution of other features such as temperature and species concentrations.

GLRT‐based detection of targets composed of distributed scattering centres

AbstractAttributed scattering problems have been found to be helpful in inverse synthetic aperture radar (ISAR) imaging and target recognition problems. In this model, the scattering centres are divided into two categories: localised and distributed. Localised scattering centres are those that are concentrated in a small area, while distributed scattering centres are spread out over a larger area. Several methods have been proposed to estimate the scattering centres which aim to accurately identify the location and characteristics of the scattering centres. However, detecting a distributed scattering centre remains a challenging task. A novel technique is proposed based on sparse signals to improve the detection of distributed scattering centres from localised ones. This technique takes advantage of the sparsity of the signals to accurately identify the location of the distributed scattering centres. Experimental results demonstrate the superiority of algorithm in detecting distributed scattering centres. This improved detection capability has significant implications for ISAR imaging and target recognition problems.

Using traffic assignment models to assist Bayesian inference for origin–destination matrices

Estimation of traffic volumes between each origin and destination of travel is a standard practice in transport engineering. Commonly the available data constitute traffic counts at various locations on the network, supplemented by a survey-based prior estimate of mean origin–destination traffic volumes. Statistical inference in this type of network tomography problem is known to be challenging. Moreover, the difficulties are increased in practice by the presence of a large number of nuisance parameters corresponding to route choice probabilities, for which we have no direct prior information. Working in a Bayesian framework, we determine these parameters using a stochastic user equilibrium route choice model. We develop an MCMC algorithm for model fitting. This requires repeated computation of stochastic user equilibrium flows, and so we develop a computationally cheap emulator. Our methods are tested on numerical examples based on a section of the road network in the English city of Leicester.

WELL-TO-WELL (W2W) ELECTROMAGNETIC TOMOGRAPHY MODELING ADVANCEMENT: IMPROVING PRECISION AND EFFECTIVENESS WITH REGULARIZATION

This research describes the direct and inverse problems of cross-well electromagnetic tomography. The model geometry has azimuthal symmetry, which simplifies the forward modeling and the inversion procedure. In the direct problem, the finite element method is used in the numerical solution of the Helmholtz equation. In the inverse problem, the study discusses the use of three stabilizer functionals: Global Smoothness (GS), Total Variation (TV), and Absolute Equality (AE). The first uses a smoothing function in the L2 norm, while the latter uses smoothing in the L1 norm, for it accepts abrupt changes between adjacent parameters. The results show that the TV method generated good estimates of both geometry and conductivity of the bodies, both for small and large conductivity contrast between the targets and the surrounding environment. Through the results, one can also observe that the regularization of the Total Variation presented a better estimate of the parameters than the Global Smoothness. In most of the synthetic models used in this work, the best estimates of the proposed model occurred when Absolute Equality constraints were used on the cells at the edges of the inversion grid, in addition to the stabilizer functional.

Artifacts of Reconstructed Images in Inverse Problems of Ultrasound Tomography in Models with Absorption

Artifacts of Reconstructed Images in Inverse Problems of Ultrasound Tomography in Models with Absorption

Modified snake optimizer based multi-level thresholding for color image segmentation of agricultural diseases

Image segmentation is crucial for early identification and diagnosis of crop diseases in agriculture. It helps identify disease areas and characteristics, laying the groundwork for disease diagnosis and treatment. However, color image thresholding segmentation method faces challenges in finding the optimal threshold as the number of thresholds increases, which affects segmentation quality. In this paper, a modified snake optimizer (MSO) is proposed to address the color image thresholding segmentation problem using Kapur’s entropy as the objective function. MSO incorporates a dynamic adaptive parameter adjustment method. The improved global position update formula introduces guidance from the optimal individual and disturbance from random individuals, effectively achieving a balance between global and local search capabilities. A dynamic parameter adjustment method is added to accelerate convergence speed during snake movement to food. Lévy flight is introduced to the Flight mode to help the algorithm escape local extremums. A balancing strategy is implemented in the Mating mode, incorporating guidance from the optimal individual and information exchange between sub-populations, enabling balanced exploration and local exploitation capabilities. The hill-climbing jump operation for the optimal individual is added to help the algorithm overcome local stagnation. The proposed MSO is evaluated on CEC 2017 test functions and color test images, and the obtained results are compared with other segmentation methods and other intelligent optimization algorithms. The results demonstrate that MSO exhibits stable and excellent performance in both complex global optimization problems and color image thresholding segmentation problems. Finally, MSO is applied to segment rice disease images, and the results reveal significantly better segmentation performance compared to other algorithms. MSO shows great potential in solving the thresholding segmentation problem for agricultural disease images, thereby assisting in the accurate identification, diagnosis, and treatment of agricultural diseases and insect pests.

Sosial Media Effect terhadap Mental Health Adolescent di Tengah Transformasi Digital: Studi Komprehensif tentang Psikologis dan Risiko Terkait

This paper examined the role of social media on mental health amidst the acceleration of digital transformation. The increasing engagement of young people in social media platforms has raised major concerns regarding its impact on psychological well-being. This study investigates that there is a strong correspondence of social media consumption and the emergence of mental health issues such as depression and anxiety and social isolation among adolescents. Data shows a significant increase in screen/mobile time among Indonesian adolescents, with average daily use exceeding global recommendations and correlating with high incidence of mental health disorders. This study utilized a comprehensive review of the existing literature, as well as engaging multiple academic sources to assess the cumulative impact of the digital Effect. Key findings suggest that excessive interaction with digital platforms potentially increases risk of cyberbullying, body image problems, depression, anxiety, self-harm, suicidal tendencies and substance consumption. Another finding, that exposure to sexual content or pornographic sites on the internet, is a predictor of increased sexual activity and premarital sex. This is an important concern, highlighting the adverse impact of prolonged digital effects on adolescents' mental resilience and life satisfaction. This research underscores the urgent need for strategic interventions and policy frameworks to mitigate the negative effects of social media on mental health holistically.

Pulmonary PET /CT image instance segmentation based on dense interactive feature fusion Mask RCNN

There are some problems in positron emission tomography/ computed tomography (PET/CT) lung images, such as little information of feature pixels in lesion regions, complex and diverse shapes, and blurred boundaries between lesions and surrounding tissues, which lead to inadequate extraction of tumor lesion features by the model. To solve the above problems, this paper proposes a dense interactive feature fusion Mask RCNN (DIF-Mask RCNN) model. Firstly, a feature extraction network with cross-scale backbone and auxiliary structures was designed to extract the features of lesions at different scales. Then, a dense interactive feature enhancement network was designed to enhance the lesion detail information in the deep feature map by interactively fusing the shallowest lesion features with neighboring features and current features in the form of dense connections. Finally, a dense interactive feature fusion feature pyramid network (FPN) network was constructed, and the shallow information was added to the deep features one by one in the bottom-up path with dense connections to further enhance the model's perception of weak features in the lesion region. The ablation and comparison experiments were conducted on the clinical PET/CT lung image dataset. The results showed that the APdet, APseg, APdet_s and APseg_s indexes of the proposed model were 67.16%, 68.12%, 34.97% and 37.68%, respectively. Compared with Mask RCNN (ResNet50), APdet and APseg indexes increased by 7.11% and 5.14%, respectively. DIF-Mask RCNN model can effectively detect and segment tumor lesions. It provides important reference value and evaluation basis for computer-aided diagnosis of lung cancer.

TLTNet: A novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation

Extracting global and local feature information is still challenging due to the problems of retinal blood vessel medical images like fuzzy edge features, noise, difficulty in distinguishing between lesion regions and background information, and loss of low-level feature information, which leads to insufficient extraction of feature information. To better solve these problems and fully extract the global and local feature information of the image, we propose a novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation, which consists of an encoder and a decoder and is connected between the encoder and decoder by a transscale transformer cascade module. Among them, the encoder consists of a local–global transscale transformer module, a multi-head layered transscale adaptive embedding module, and a local context(LCNet) module. The transscale transformer cascade module learns local and global feature information from the first three layers of the encoder, and multi-scale dependent features, fuses the hierarchical feature information from the skip connection block and the channel-token interaction fusion block, respectively, and inputs it to the decoder. The decoder includes a decoding module for the local context network and a transscale position transformer module to input the local and global feature information extracted from the encoder with retained key position information into the decoding module and the position embedding transformer module for recovery and output of the prediction results that are consistent with the input feature information. In addition, we propose an improved cross-entropy loss function based on the difference between the deterministic observation samples and the prediction results with the deviation distance, which is validated on the DRIVE and STARE datasets combined with the proposed network model based on the dual transformer structure in this paper, and the segmentation accuracies are 97.26% and 97.87%, respectively. Compared with other state-of-the-art networks, the results show that the proposed network model has a significant competitive advantage in improving the segmentation performance of retinal blood vessel images.