Types Of Image Data Research Articles

A prediction model for the mechanical properties of SUS316 stainless steel ultrathin strip driven by multimodal data mixing

Constructing a mapping relationship among material preparation process, microstructure, and mechanical properties is a challenge in material research and development. In this work, a deep learning framework for multimodal data fusion is constructed that couples a multi-layer perceptron (MLP) and a residual neural network (ResNet) to predict mechanical properties of SUS316 stainless steel ultrathin strips. Specifically, the MLP branch is used to extract the rolling process data features, and the ResNet with the addition of a convolutional block attention module (CBAM) is used to extract the microstructure features. Six models are constructed for comparison under the comprehensive consideration of factors such as unimodal network, the multimodal network and input form of image samples. The results show that the multimodal data model fused with the ResNet and MLP after adding the CBAM using both rolling process data and four types of microstructure image data as model inputs has the most accurate prediction results. The R2, MAPE, RMSE and MAE are 0.998, 0.727, 4.440 and 3.359, respectively. In addition, the proposed model is used for predicting yield strength and elongation, and the results show that the R2 values of both models on the test set are greater than 0.980, fully confirming that the multimodal data model has high prediction accuracy and good generalizability.

Deep Learning Methods for Chest X-Ray Imaging-Based COVID-19 Pneumonia Detection

The COVID-19 pandemic currently underway has highlighted a need for fast and accurate screening tools to help identify the disease, particularly in resource-poor settings. In this paper, a deep convolutional neural network (CNN) model is proposed for the automatic detection of COVID-19 pneumonia using chest X-ray images that helps in reducing the cost and processing time compared to traditional testing procedures. It uses several deep-structured medical image data types and improvement of the deep learning model architecture to achieve good diagnostic accuracy. Our hybrid CNN architecture integrates VGG-16 for feature extraction and ResNet-50 for pattern complexity assessment in detecting fine changes characteristic of COVID-19 pneumonia. Our dataset for this study is a collection of few thousands labelled chest X-ray images categorized in three classes, which are COVID-19, viral pneumonia and healthy cases. More advanced data preprocessing methods such as normalized, augmentation, and filtered noise has also been carried out which enhances in the model performance. We have high accuracy, recall, and good F1-score in the experimental results proving model robustness are applicable in real-world clinical scenarios. These research results speak to the importance of AI for improving diagnosis workflows, especially in fast-to-deploy large scale scenarios needed during a pandemic to cope with healthcare burdens.

Comparative analysis of brain age prediction using structural and diffusion MRIs in neonates

Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

A novel semi-supervised deep learning method for enhancing discriminability and diversity in EEG-based emotion recognition task

Numerous deep learning models have been introduced for EEG-based Emotion recognition tasks. Nevertheless, the majority of these models are fully supervised, demanding substantial amounts of labeled EEG signals. The labeling process of EEG signals is both time-intensive and costly, involving numerous trials and meticulous analysis by experts. Recently, some advanced semi-supervised algorithms that can achieve a competitive performance with fully-supervised methods by using only a small set of labeled data have been presented. However, these algorithms are primarily developed for the image data type, and naïve adaptation of them for EEG applications results in unsatisfactory performance. To address this issue, we present a robust semi-supervised EEG-based method that exploits the best techniques from advanced semi-supervised algorithms in the computer vision domain enriched with novel regularization terms for unlabeled signals. The proposed regularization terms improve both the discriminability and diversity of the model’s predictions and effectively leverage prior knowledge about the class distributions, thereby achieving a superior performance compared to the distribution alignment techniques in state-of-the-art methods. We evaluate our method on the DEAP dataset for cross-subject valence/arousal emotion recognition tasks, and on the SEED in a cross-session setting. The results indicate that the proposed method consistently surpasses the peer methods at different numbers of labeled data by a large margin.

A Machine learning approach for Post-Disaster data curation

Image data collected after natural disasters play an important role in the forensics of structure failures. However, curating and managing large amounts of post-disaster imagery data is challenging. In most cases, data users still have to spend much effort to find and sort images from the massive amounts of images archived for past decades in order to study specific types of disasters. This paper proposes a new machine learning based approach for automating the labeling and classification of large volumes of post-natural disaster image data to address this issue. More specifically, the proposed method couples pre-trained computer vision models and a natural language processing model with an ontology tailed to natural disasters to facilitate the search and query of specific types of image data. The resulting process returns each image with five primary labels and similarity scores, representing its content based on the developed word-embedding model. Validation and accuracy assessment of the proposed methodology was conducted with ground-level residential building panoramic images from Hurricane Harvey. The computed primary labels showed a minimum average difference of 13.32% when compared to manually assigned labels. This versatile and adaptable solution offers a practical and valuable solution for automating image labeling and classification tasks, with the potential to be applied to various image classifications and used in different fields and industries. The flexibility of the method means that it can be updated and improved to meet the evolving needs of various domains, making it a valuable asset for future research and development.

Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis.

Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87-96, p< 0.01) and specificity (90%; 95% CI: 85-93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90-94, p< 0.01; and 91%; 95% CI: 88-93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77-85, p< 0.01; and 83%; 95% CI: 76-88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90-96, p < 0.01) and specificity (92%; 95% CI: 89-94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).

Innovative design of digital sculpture art under 3D modeling technology

Abstract 3D modeling technology is one of the most important research directions in computer vision, which is increasingly linked with digital sculpture art. Combined with the point cloud, a new type of image data, this paper innovatively proposes a multi-view 3D digital sculpture reconstruction model to realize the 3D digital sculpture reconstruction of point cloud data.3D digital sculpture reconstruction is mainly divided into two parts: point cloud data processing and 3D reconstruction. The former uses radius, statistical filtering, and bilateral filtering algorithms for point cloud data denoising and proposes an NDT alignment algorithm to integrate point cloud data. The latter triangulates the unreconstructed observation points introduced by the new image, uses beam method leveling to globally optimize the point cloud structure, and proposes incremental recovery and global recovery reconstruction methods, respectively. In the point cloud data alignment performance test, the reconstruction plotting time of the reconstruction model in this paper possesses a large improvement, and the proposed NDT algorithm outperforms the traditional algorithm in terms of accuracy and execution efficiency. In the reconstruction of architectural digital sculpture, the average error of the parts of the digital sculpture is 0.00775, and the maximum residual difference is at about 4.5 pixels. Hence, the sculpture’s accuracy and precision performance are better.

CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional network

Cylindrical organs, e.g., blood vessels, airways, and intestines, are ubiquitous structures in biomedical optical imaging analysis. Image segmentation of these structures serves as a vital step in tissue physiology analysis. Traditional model-driven segmentation methods seek to fit the structure by constructing a corresponding topological geometry based on domain knowledge. Classification-based deep learning methods neglect the geometric features of the cylindrical structure and therefore cannot ensure the continuity of the segmentation surface. In this paper, by treating the cylindrical structures as a 3D graph, we introduce a novel contour-based graph neural network for 3D cylindrical structure segmentation in biomedical optical imaging. Our proposed method, which we named CylinGCN, adopts a novel learnable framework that extracts semantic features and complex topological relationships in the 3D volumetric data to achieve continuous and effective 3D segmentation. Our CylinGCN consists of a multiscale 3D semantic feature extractor for extracting inter-frame multiscale semantic features, and a residual graph convolutional network (GCN) contour generator that combines the semantic features and cylindrical topological priors to generate segmentation contours. We tested the CylinGCN framework on two types of optical tomographic imaging data, small animal whole body photoacoustic tomography (PAT) and endoscopic airway optical coherence tomography (OCT), and the results show that CylinGCN achieves state-of-the-art performance. Code will be released at https://github.com/lzc-smu/CylinGCN.git.

Retina image segmentation using the three-path Unet model

Unsupervised image segmentation is a technique that divides an image into distinct regions or objects without prior labeling. This approach offers flexibility and adaptability to various types of image data. Particularly for large datasets, it eliminates the need for manual labeling, thereby it presents advantages in terms of time and labor costs. However, when applied to retinal image segmentation, challenges arise due to variations in data, presence of noise, and manual threshold adjustments, which can lead to over-segmentation or under-segmentation of small blood vessel boundaries and endpoints. In order to enhance the precision and accuracy of retinal image segmentation, we propose a novel image supervised segmentation network based on three-path Unet model.Firstly, the Haar wavelet transform is employed to extract high-frequency image information, which forms the foundation for the proposed HaarNet, a Unet-inspired architecture. Next, the HaarNet is integrated with the Unet and SegNet frameworks to develop a three-path Unet model, referred to as TP-Unet. Finally, the model is further refined into TP-Unet+AE+DSL by incorporating the advantages of auto-encoding (AE) and deep supervised learning (DSL) techniques, thereby enhancing the overall performance of the system. To evaluate the effectiveness of our proposed model, we conduct experiments using the DRIVE and CHASE public datasets. On the DRIVE dataset, our recommended model achieves a Dice coefficient of 0.8291 and a sensitivity index of 0.8184. These results significantly outperform the Unet model by 1.34%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.34\\%$$\\end{document} and 2.60%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.60\\%$$\\end{document}, respectively. Furthermore, our model demonstrates excellent performance on the CHASE dataset, with a Dice coefficient of 0.8162, a sensitivity of 0.8242, and an accuracy of 0.9664. These metrics surpass the Unet model by 3.20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.20\\%$$\\end{document}, 6.66%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.66\\%$$\\end{document}, and 0.42%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.42\\%$$\\end{document}, respectively. Our proposed model provides more accurate and reliable results for retinal vessel segmentation, which holds significant potential for assisting doctors in their diagnosis.

WarpDrive: Improving spatial normalization using manual refinements

Spatial normalization—the process of mapping subject brain images to an average template brain—has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.

Human Pose Estimation Using Deep Learning: A Systematic Literature Review

Human Pose Estimation (HPE) is the task that aims to predict the location of human joints from images and videos. This task is used in many applications, such as sports analysis and surveillance systems. Recently, several studies have embraced deep learning to enhance the performance of HPE tasks. However, building an efficient HPE model is difficult; many challenges, like crowded scenes and occlusion, must be handled. This paper followed a systematic procedure to review different HPE models comprehensively. About 100 articles published since 2014 on HPE using deep learning were selected using several selection criteria. Both image and video data types of methods were investigated. Furthermore, both single and multiple HPE methods were reviewed. In addition, the available datasets, different loss functions used in HPE, and pretrained feature extraction models were all covered. Our analysis revealed that Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are the most used in HPE. Moreover, occlusion and crowd scenes remain the main problems affecting models’ performance. Therefore, the paper presented various solutions to address these issues. Finally, this paper highlighted the potential opportunities for future work in this task.

Prediction of cancer recurrence based on compact graphs of whole slide images

Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients’ survival is accurately predicting recurrence status and giving appropriate treatment. Whole Slide Images (WSIs) are a common type of image data in the field of digital pathology, containing high-resolution tissue information. Furthermore, WSIs of primary tumors contain microenvironmental information directly associated with the growth of tumor cells. To effectively utilize this microenvironmental information. Firstly, we represented microenvironmental features of histopathological images as compact graphs. Secondly, this work aims to develop an enhanced lightweight graph neural network called the Adaptive Graph Clustering Network (AGCNet) for predicting cancer recurrence. Experiments are conducted on three cancer datasets from The Cancer Genome Atlas (TCGA), and AGCNet achieved an accuracy of 81.81% in BLCA, 69.66% in PAAD, and 81.96% in STAD. These results indicated that AGCNet is an effective model for predicting cancer recurrence and is expected to be applied in clinical applications.

Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network

Studying domain adaptation is a recent research trend. Generally, many generative models that researchers have studied perform well on training data from a specific domain. However, their ability to be generalized to other domains might be limited. Therefore, a growing body of research has utilized domain adaptation techniques to address the problem of generative models being vulnerable to input from other domains. In this paper, we focused on generative models and representation learning. Generative models have received a lot of attention for their ability to generate various types of data such as images, music, and text. In particular, studies utilizing generative adversarial neural networks (GANs) and autoencoder structures have received a lot of attention. In this paper, we solved the domain adaptation problem by reconstructing real image data using an autoencoder structure. In particular, reconstructed image data, considered a type of noisy image data, are used as input data. How to reconstruct data by extracting features and selectively transforming them in order to reduce differences in characteristics between domains entails representative learning. Considering these research trends, this paper proposed a novel methodology combining bidirectional feature learning and generative networks to innovatively approach the domain adaptation problem. It could improve the adaptation ability by accurately simulating the real data distribution. The experimental results show that the proposed model outperforms the traditional DANN and ADDA. This demonstrates that combining bidirectional feature learning and generative networks is an effective solution in the field of domain adaptation. These results break new ground in the field of domain adaptation. They are expected to provide great inspiration for future research and applications. Finally, through various experiments and evaluations, we verify that the proposed approach outperforms the existing works. We conducted experiments for representative generative models and domain adaptation techniques and found that the proposed approach was effective in improving data and domain robustness. We hope to contribute to the development of domain-adaptive models that are robust to the domain.

ProMIL: A weakly supervised multiple instance learning for whole slide image classification based on class proxy

The histopathological analysis of a suspected region is critical for cancer diagnosis, treatment, and management. Histopathological diagnosis consists in analyzing the characteristics of the lesions using tissue sections stained with hematoxylin and eosin. Classification of digital tumor pathology images, called whole slide images (WSIs), is a great challenge since WSIs usually have huge resolutions while lacking localized annotations. Multiple instance learning (MIL) is a commonly used method applied to pathological image analysis. However, most MIL methods often focus only on the global representation of WSIs, ignoring whether the category labels play other roles in the model training besides being a supervision signal. In addition, feature confusion is also a problem that should be avoided for the analysis of WSIs with weakly supervised methods. To address these problems, we propose a novel algorithm of classifying WSI for cancer diagnosis. The proposed model, ProMIL, uses only slide-level labels rather than localized annotations for analysis. There are three innovations in this work. Firstly, we present the concept of class proxy which is the representation of the intrinsic feature of each category, and plays a key role in guiding the training of the model. Secondly, we design a novel WSI representation learning module that utilizes a multi-scale feature extraction strategy to represent each patch in a WSI and then aggregates these representations using an attention mechanism to encode the WSI. Thirdly, we design a metric-learning-based weakly supervised multiclass-classifier by measuring the similarity between each WSI embedding and class proxies. The proposed ProMIL can effectively alleviate the side effect of feature confusion, and carry intuitive interpretability and scalability. To evaluate the performance of ProMIL, we conduct a series of experiments on several datasets of WSIs with different types of cancer from open data sources. It can be observed from the experimental results that ProMIL outperforms most of the compared methods and achieves better performance on a various type of cancer image data for classification, thus suggesting the proposed method is suitable for classifying different categories of cancer rather than a specific kind of cancer. Therefore, it is expected to act as a general framework to be extended to more cancer diagnoses.

SE-YOLOv7 Landslide Detection Algorithm Based on Attention Mechanism and Improved Loss Function

With the continuous development of computer vision technology, more and more landslide identification detection tasks have started to shift from manual visual interpretation to automatic computer identification, and automatic landslide detection methods based on remote sensing satellite images and deep learning have been gradually developed. However, most existing algorithms often have the problem of low precision and weak generalization in landslide detection. Based on the Google Earth Engine platform, this study selected landslide image data from 24 study areas in China and established the DN landslide sample dataset, which contains a total of 1440 landslide samples. The original YOLOv7 algorithm model was improved and optimized by applying the SE squeezed attention mechanism and VariFocal loss function to construct the SE-YOLOv7 model to realize the automatic detection of landslides in remote sensing images. The experimental results show that the mAP, Precision value, Recall value, and F1-Score of the improved SE-YOLOv7 model for landslide identification are 91.15%, 93.35%, 94.54%, and 93.94%, respectively. At the same time, through a field investigation and verification study in Qianyang County, Baoji City, Shaanxi Province, comparing the detection results of SE-YOLOv7, it is concluded that the improved SE-YOLOv7 can locate the landslide location more accurately, detect the landslide range more accurately, and have fewer missed detections. The research results show that the algorithm model has strong detection accuracy for many types of landslide image data, which provides a technical reference for future research on landslide detection based on remote sensing images.

Nondestructive detection of lipid oxidation in frozen pork using hyperspectral imaging technology

Lipid oxidation is an important cause of pork quality degradation during freezing. Traditional chemical methods are time-consuming and destructive. In this paper, two hyperspectral imaging (HSI) techniques, including visible near-infrared (vis-NIR HSI) (400–1002 nm) and fluorescence (F-HSI) (400–1002 nm), were tested for non-destructive detection of lipid oxidation in pork. Two types of hyperspectral image data were collected from pork samples with 0–9 freeze-thaw cycles. The model performance based on the two spectra data was then tested by three multivariate analysis methods of partial least squares regression, support vector regression and Gaussian process regression (GPR). It was found that the GPR models using vis-NIR HSI and F-HSI acquired optimal prediction results of R2p = 0.9697, RMSEP= 0.0184 mg/kg and R2p = 0.9726, RMSEP= 0.0182 mg/kg, respectively. The results showed that the two techniques have shown reliable performance in predicting TBARS, and the performance of F-HSI was slightly superior to vis-NIR HSI. A pseudo-color map of TBARS was drawn using the F-HSI model to provide a visual screening method for lipid oxidation in pork. Moreover, another batch of pork with different freeze-thaw cycles (0–5 cycles) was successfully quantified and visualized for TBARS content using the F-HSI model. It demonstrated the feasibility of using F-HSI for quantitative monitoring of lipid oxidation in pork.

Local energy-based image region segmentation using Legendre polynomials

Segmentation of images is a critical pre-processing step in image processing. The objective of segmentation is to divide an image into two homogeneous segments based on similar features. Recently, energy-based segmentation methods, such as the level set algorithm, have exhibited remarkable performance in segmenting various types of image data. However, such methods fail to produce satisfactory results on textured images, leading to inaccurate foreground and background segmentation. To address this issue, the proposed method utilizes a feature space based on image local energy, which is independent of image intensity. This feature space provides a more robust representation of texture in the image. By taking advantage of a unique feature space, images can be represented with a sum of Legendre polynomials. The reproduced image is injected into the L2S algorithm. The level set function is optimized from the average difference of Legendre polynomial coefficients to provide the best representation of the image. Consequently, the proposed method results in better segmentation accuracy, with an average increase of 19.33 compared to existing state-of-the-art methods.

Identification of blurred terahertz images by improved cross-layer convolutional neural network.

Terahertz imaging technology has been gradually used in space communication, radar detection, aerospace and biomedical fields. Nevertheless, there are still some limits in terahertz image, such as single tone, fuzzy texture features, poor image resolution and less data, which seriously affect the application and popularization of Terahertz image technology in many fields. Traditional convolutional neural network (CNN) is an effective method for image recognition, but it is limited in highly blurred terahertz image recognition due to the great difference between terahertz image and traditional optical image. This paper presents a proven method for higher recognition rate of blurred terahertz images by using an improved Cross-Layer CNN model with different definition terahertz image dataset. Compared to employing clear image dataset, the accuracy of blurred image recognition can be improved from about 32% to 90% with different definition dataset. Meanwhile, the recognition accuracy of high blurred image can be improved by approximately 5% in contrast to the traditional CNN, which makes the higher recognition ability of neural network. It can be demonstrated that various types of blurred terahertz imaging data can be effectively identified by constructing different definition dataset combined with Cross-Layer CNN. A new method is proved to improve the recognition accuracy of terahertz imaging and application robustness in real scenarios.

Image Haze Removal Method Based on Histogram Gradient Feature Guidance.

Optical remote sensing images obtained in haze weather not only have poor quality, but also have the characteristics of gray color, blurred details and low contrast, which seriously affect their visual effect and applications. Therefore, improving the image clarity, reducing the impact of haze and obtaining more valuable information have become the important aims of remote sensing image preprocessing. Based on the characteristics of haze images, combined with the earlier dark channel method and guided filtering theory, this paper proposed a new image haze removal method based on histogram gradient feature guidance (HGFG). In this method, the multidirectional gradient features are obtained, the atmospheric transmittance map is modified using the principle of guided filtering, and the adaptive regularization parameters are designed to achieve the image haze removal. Different types of image data were used to verify the experiment. The experimental result images have high definition and contrast, and maintain significant details and color fidelity. This shows that the new method has a strong ability to remove haze, abundant detail information, wide adaptability and high application value.

Martensite Start Temperature Prediction through a Deep Learning Strategy Using Both Microstructure Images and Composition Data.

In recent decades, various previous research has established empirical formulae or thermodynamic models for martensite start temperature (Ms) prediction. However, most of this research has mainly considered the effect of composition and ignored complex microstructural factors, such as morphology, that significantly affect Ms. The main limitation is that most microstructures cannot be digitized into numerical data. In order to solve this problem, a convolutional neural network model that can use both composition information and microstructure images as input was established for Ms prediction in a medium-Mn steel system in this research. Firstly, the database was established through experimenting. Then, the model was built and trained with the database. Finally, the performance of the model was systematically evaluated based on comparison with other, traditional AI models. It was proven that the new model provided in this research is more rational and accurate because it considers both composition and microstructural factors. In addition, because of the use of microstructure images for data augmentation, the deep learning had a low risk of overfitting. When the deep-learning strategy is used to deal with data that contains both numerical and image data types, obtaining the value matrix that contains interaction information of both numerical and image data through data preprocessing is probably a better approach than direct linking of the numerical data vector to the fully connected layer.