Enhancing low-light images with natural colors poses a challenge due to camera processing variations and limited access to ground-truth lighting conditions. To address this, we propose Dimma, a semi-supervised approach that aligns with any camera usi...
The classification of aerial scenes has been extensively studied as the basic work of remote sensing image processing and interpretation. However, the performance of remote sensing image scene classification based on deep neural networks is limited b...
In recent years, contrastive learning has been a highly favored method for self-supervised representation learning, which significantly improves the unsupervised training of deep image models. Self-supervised learning is a subset of unsupervised lear...
In recent years, fully supervised object detection methods in remote sensing images with good performance have been developed. However, this approach requires a large number of instance-level annotated samples that are relatively expensive to acquire...
Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics...
This paper proposes a supervised deep neural network model for accomplishing highly efficient image quality assessment (IQA) for adaptive optics (AO) images. The AO imaging systems based on ground-based telescopes suffer from residual atmospheric tur...
Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component...
A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is qu...
In order to solve the problem of manual labeling in semi-supervised tree species classification, this paper proposes a pixel-level self-supervised learning model named M-SSL (multisource self-supervised learning), which takes the advantage of the inf...
Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model...
The interpretation of optical and synthetic aperture radar (SAR) images in remote sensing is general for many tasks, such as environmental monitoring, marine management, and resource planning. Instance segmentation of optical and SAR images, which ca...
In large solar farms, supervision is an exhaustive task, often carried out manually by field technicians. Over time, automated or semi-automated fault detection and prevention methods in large photovoltaic plants are becoming increasingly common. The...
Cloud detection methods based on deep learning depend on large and reliable training datasets to achieve high detection accuracy. There will be a significant impact on their performance, however when the training data are insufficient or when the lab...
The radiometric terrain correction (RTC) is an essential processing step for supervised classification applications of polarimetric synthetic aperture radar (PolSAR) over mountainous areas. However, the current angular variation effect (AVE) correcti...
In recent years, the application of deep learning has achieved a huge leap in the performance of remote sensing image super-resolution (SR). However, most of the existing SR methods employ bicubic downsampling of high-resolution (HR) images to obtain...
Automated perimetrium segmentation of transvaginal ultrasound images is an important process for computer-aided diagnosis of uterine diseases. However, ultrasound images often contain various structures and textures, and these structures have differe...
Industrial anomaly detection is constrained by the scarcity of anomaly samples, limiting the applicability of supervised learning methods. Many studies have focused on anomaly detection by generating anomaly images and adopting self-supervised learni...
Machine vision has significant advantages in a wide range of agricultural applications; however, acquiring a large number of high-quality image resources is often challenging in actual agricultural production due to environmental and equipment condit...
Semi-supervised learning aims to improve the generalization performance of a model by exploiting the large quantity of unlabeled data together with limited labeled data during training. When applied to object detection in remote sensing images, semi-...
The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train...
Image aesthetic quality assessment (IAQA) has aroused considerable interest in recent years and is widely used in various applications, such as image retrieval, album management, chat robot and social media. However, existing methods need an excessiv...
Image denoising, a classic ill-posed problem, aims to recover a latent image from a noisy measurement. Over the past few decades, a considerable number of denoising methods have been studied extensively. Among these methods, supervised deep convoluti...
Effectively managing the quality of iron ore is critical to iron and steel metallurgy. Although quality inspection is crucial, the perspective of sintered surface identification remains largely unexplored. To bridge this gap, we propose a deep learni...
Cross-spectral local feature matching between visual and thermal images benefits many vision tasks in low-light environments, including image-to-image fusion and camera re-localization. An essential prerequisite for unleashing the potential of superv...
Show-through phenomena have always been a challenging issue in color-document image processing, which is widely used in various fields such as finance, education, and administration. Existing methods for processing color-document images face challeng...
Clouds have an enormous influence on the hydrological cycle, Earth’s radiation budget, and climate changes. Accurate automatic recognition of cloud shape based on ground-based cloud images is beneficial to analyze the atmospheric motion state a...
Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large dataset...
The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25...
Because there are many unlabeled samples in hyperspectral images and the cost of manual labeling is high, this paper adopts semi-supervised learning method to make full use of many unlabeled samples. In addition, those hyperspectral images contain mu...
With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The prem...
Agricultural policies have impacts on land use, the economy, and the environment and their analysis requires disaggregated data at the local level with geographical references. Thus, this study proposes a model for disaggregating agricultural data, w...
Data scarcity poses a significant challenge in medical image segmentation, thereby highlighting the importance of leveraging sparse annotation data. In addressing this issue, semi-supervised learning has emerged as an effective approach for training...
Phase retrieval from supervised learning neural networks is restricted due to the problem of obtaining labels. To address this situation, in the present paper, we propose a phase retrieval model of self-supervised physical deep learning combined with...
Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning...
In recent research, supervised image clustering based on Graph Neural Networks (GNN) connectivity prediction has demonstrated considerable improvements over traditional clustering algorithms. However, existing supervised image clustering algorithms a...
The segmentation of crop disease zones is an important task of image processing since the knowledge of the growth status of crops is critical for agricultural management. Nowadays, images taken by unmanned aerial vehicles (UAVs) have been widely used...
The task of semantic segmentation of maize and weed images using fully supervised deep learning models requires a large number of pixel-level mask labels, and the complex morphology of the maize and weeds themselves can further increase the cost of i...
Image manipulation localization is one of the most challenging tasks because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned. Unlike many existing solutions, we employ a sema...
Ischemic stroke lesion segmentation using different types of images, such as Computed Tomography Perfusion (CTP), is important for medical and Artificial intelligence fields. These images are potential resources to enhance machine learning and deep l...
Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass...
In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the...
Due to the incomplete nature of cognitive testing data and human subjective biases, accurately diagnosing mental disease using functional magnetic resonance imaging (fMRI) data poses a challenging task. In the clinical diagnosis of mental disorders,...
The role of image data in knowledge extraction and representation has become increasingly significant. This study introduces a novel methodology, termed Image to Graph via Large Language Model (ImgGraph-LLM), which constructs a knowledge graph for ea...
To address the prevalent noise issue in images generated by HumanNeRF, this paper proposes an image denoising method that combines self-supervised contrastive learning and Generative Adversarial Networks (GANs). While HumanNeRF excels in realistic 3D...
A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a m...
Digital image devices have been widely applied in many fields, such as individual recognition and remote sensing. The captured image is a degraded image from the latent observation, where the degradation processing is affected by some factors, such a...
In railway surface defect detection applications, supervised deep learning methods suffer from the problems of insufficient defect samples and an imbalance between positive and negative samples. To overcome these problems, we propose a lightweight tw...
Pine wilt disease (PWD) is the most dangerous biohazard of pine species and poses a serious threat to forest resources. Coupling satellite remote sensing technology and deep learning technology for the accurate monitoring of PWD is an important tool...
To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size...
Existing deepfake detection methods heavily rely on specific training data distributions and struggle to generalize to unknown forgery techniques. To address the challenge, this paper focuses on two critical research gaps: (1) the lack of systematic...
of 23