In this paper, we consider the problem of feature reconstruction from incomplete X-ray CT data. Such incomplete data problems occur when the number of measured X-rays is restricted either due to limit radiation exposure or due to practical constraint...
In recent years, great progress has been made in the field of super-resolution (SR) reconstruction based on deep learning techniques. Although image SR techniques show strong potential in image reconstruction, the effective application of these techn...
At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the hu...
Amidst the dual challenges of energy shortages and global warming, photovoltaic (PV) power generation has emerged as a critical technology due to its efficient utilization of solar energy. Rooftops, as underutilized spaces, are ideal locations for in...
The main challenges in reconstruction-based anomaly detection include the breakdown of the generalization gap due to improved fitting capabilities and the overfitting problem arising from simulated defects. To overcome this, we propose a new method c...
In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature residual network. In this method, the...
In the task of integrated circuit micrograph acquisition, image super-resolution reconstruction technology can significantly enhance acquisition efficiency. With the advancement of deep learning techniques, the performance of image super-resolution r...
In the incipient fault vibration signals of rolling bearings, weak fault features are easily submerged in strong background noise and difficult to be extracted. The sparse decomposition method can perform well in the extraction of weak fault features...
Image super-resolution reconstruction achieves better results than traditional methods with the help of the powerful nonlinear representation ability of convolution neural network. However, some existing algorithms also have some problems, such as in...
Road defect detection is essential for timely road damage repair and traffic safety assurance. However, existing object detection algorithms suffer from insufficient accuracy in detecting small road surface defects and are prone to missed detections...
Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previo...
The fast and accurate detection of wind turbine gearbox surface defects is crucial for wind turbine maintenance and power security. However, owing to the uneven distribution of gear surface defects and the interference of complex backgrounds, there a...
Deep learning has excelled in image classification largely due to large, professionally labeled datasets. However, in the field of medical images data annotation often relies on experienced experts, especially in tasks such as white blood cell classi...
With the widespread application of facial image-based age estimation technologies in fields such as marketing, medical aesthetics, and intelligent surveillance, their importance has become increasingly evident. However, in real-world scenarios, the f...
Fine-grained image classification is faced with the challenge of significant intra-class differences and subtle similarities between classes, with a limited number of labelled data. Previous few-shot learning approaches, however, often fail to recogn...
Feature selection has been widely used in machine learning and data mining since it can alleviate the burden of the so-called curse of dimensionality of high-dimensional data. However, in previous works, researchers have designed feature selection me...
In recent years, deep convolutional neural networks (CNNs) have made significant progress in single-image super-resolution (SISR) tasks. Despite their good performance, the single-image super-resolution task remains a challenging one due to problems...
Interacting hand reconstruction presents significant opportunities in various applications. However, it currently faces challenges such as the difficulty in distinguishing the features of both hands, misalignment of hand meshes with input images, and...
In cross-corpus scenarios, inappropriate feature-processing methods tend to cause the loss of key emotional information. Additionally, deep neural networks contain substantial redundancy, which triggers domain shift issues and impairs the generalizat...
Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition a...
In recent years, few-shot fine-grained image classification has shown great potential in addressing data scarcity and distinguishing highly similar categories. However, existing unidirectional reconstruction methods, while enhancing inter-class diffe...
High-fidelity mesh reconstruction from point clouds has long been a fundamental research topic in computer vision and computer graphics. Traditional methods require dense triangle meshes to achieve high fidelity, but excessively dense triangles may l...
Stock volatility is an important measure of financial risk. Due to the complexity and variability of financial markets, time series forecasting in the financial field is extremely challenging. This paper proposes a “model fusion learning algori...
Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integr...
When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper expl...
Estimating the state of health of lithium-ion batteries in energy storage systems is a key step in their subsequent safety monitoring and energy optimization management. This study proposes a method for estimating the state of health of lithium-ion b...
The underwater images acquired by marine detectors inevitably suffer from quality degradation due to color distortion and the haze effect. Traditional methods are ineffective in removing haze, resulting in the residual haze being intensified during c...
The existing thermal infrared (TIR) ship detection methods may suffer serious performance degradation in the situation of heavy sea clutter. To cope with this problem, a novel ship detection method based on morphological reconstruction and multi-feat...
Three-dimensional (3D) point clouds have a wide range of applications in the field of 3D vision. The quality of the acquired point cloud data considerably impacts the subsequent work of point cloud processing. Due to the sparsity and irregularity of...
The fault response signals of an axle-box bearing of a rail vehicle have strongly non-linear and non-stationary characteristics, which can reflect the operating state of the running gears. This paper proposes a novel method for bearing fault diagnosi...
Existing methods for reconstructing hyperspectral images from single RGB images struggle to obtain a large number of labeled RGB-HSI paired images. These methods face issues such as detail loss, insufficient robustness, low reconstruction accuracy, a...
Three-dimensional (3D) reconstruction is an essential technique to visualize and monitor the growth of agricultural and forestry plants. However, inspecting tall plants (trees) remains a challenging task for single-camera systems. A combination of lo...
As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple arch...
Optical imagery is often contaminated by clouds to varying degrees, which greatly affects the interpretation and analysis of images. Synthetic Aperture Radar (SAR) possesses the characteristic of penetrating clouds and mist, and a common strategy in...
Dense 3D reconstruction from multi-aspect angle synthetic aperture radar (SAR) imagery has gained considerable attention for urban monitoring applications. However, achieving reliable dense matching between multi-aspect SAR images remains challenging...
The vertical structure of radar echo is crucial for understanding complex microphysical processes of clouds and precipitation, and for providing essential data support for the study of low-level wind shear and turbulence formation, evolution, and dis...
The target spectrum, which is commonly used in feature extraction for underwater acoustic target classification, can be improperly recovered via conventional beamformer (CBF) owing to its frequency-variant spatial response and lead to degraded classi...
In environments characterised by noise or the absence of audio signals, visual cues, notably facial and lip movements, serve as valuable substitutes for missing or corrupted speech signals. In these scenarios, speech reconstruction can potentially ge...
In order to address the problem of the uncertainty of existing noise models and of the complexity and changeability of the edges and textures of low-resolution document images, this paper presents a projection onto convex sets (POCS) algorithm based...
As the traditional single camera endoscope can only provide clear images without 3D measurement and 3D reconstruction, a miniature binocular endoscope based on the principle of binocular stereoscopic vision to implement 3D measurement and 3D reconstr...
High-quality multispectral LiDAR (MSL) data are crucial for land cover (LC) classification. However, the Titan MSL system encounters challenges of inconsistent spatial–spectral information due to its unique scanning and data saving method, rest...
Acquiring high-resolution digital elevation models (DEMs) over across extensive regions remains challenging due to high costs and insufficient detail, creating demand for super-resolution (SR) techniques. However, existing DEM SR methods still rely o...
Generating artistic portraits from face photos presents a complex challenge that requires high-quality image synthesis and a deep understanding of artistic style and facial features. Traditional generative adversarial networks (GANs) have made signif...
Schizophrenia (SZ) is a severe mental disorder characterised by disruptions in cognition, behaviour, and perception, significantly impacting an individual’s life. Traditional SZ diagnosis methods are labour-intensive and prone to errors. This s...
The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same tim...
Unsupervised domain adaptation (UDA) effectively transfers knowledge learned from a labeled source domain to an unlabeled target domain. The teacher–student framework, which generates pseudo-labels for target domain samples and uses them for ps...
Geostationary satellite land surface temperature (GLST) data are important for various dynamic environmental and natural resource applications for terrestrial ecosystems. Due to clouds, shadows, and other atmospheric conditions, the derived LSTs are...
The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of alg...
Fourier ptychographic microscopy (FPM) is a potential imaging technique, which is used to achieve wide field-of-view (FOV), high-resolution and quantitative phase information. The LED array is used to irradiate the samples from different angles to ob...
Aiming to address the issue that existing multi-view stereo reconstruction methods have insufficient adaptability to the repetitive and weak textures in multi-view images, this paper proposes a three-dimensional (3D) reconstruction algorithm based on...
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