Background: Despite the development of iterative reconstruction (IR) in diagnostic imaging, CBCT are generally reconstructed with filtered back projection (FBP) in radiotherapy. Varian medical systems, recently released with their latest Halcyon®...
The purpose of this study was to compare the quality of low-energy virtual monoenergetic images (VMIs) obtained with three Dual-Energy CT (DECT) platforms according to the phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on tw...
This study aims to develop and validate software for the automatic measurement of the task-transfer function (TTF) based on the American Association of Physicists in Medicine (AAPM) Task Group (TG) 233. The software consists of two main stages: autom...
An image-quality CT phantom was scanned with three different 3D X-ray imaging guidance devices in the operating theatre: O-Arm, Loop-X, and Airo TruCT. Default acquisition and reconstruction parameters for lumbar spine procedures were used on each de...
Background: Photon-counting computed tomography (PCCT) represents a major technological advance in clinical CT imaging, offering superior spatial resolution, enhanced material discrimination, and potential radiation dose reduction compared to convent...
The deep convolutional neural network (DCNN) has recently been applied to the highly challenging and ill-posed problem of single image super-resolution (SISR), which aims to predict high-resolution (HR) images from their corresponding low-resolution...
Nonsuicidal self-injury (NSSI) is prevalent and affects mainly the youth population. It is prospectively associated with suicide attempts, making it a target for suicide prevention. Recently, several studies have investigated neural pathways of NSSI...
Task-based image quality assessment—the modulation transfer function, the noise power spectrum (NPS), the noise-equivalent quanta and model observers—fails by returning a plausible wrong number rather than an error, and the regression tes...
No reference image quality assessment is a technique that uses computers to simulate the human visual system and automatically evaluate the perceived quality of images. In recent years, with the widespread success of deep learning in the field of com...
When image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information about the image, utilizes contextual infor...
With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Althoug...
Many existing natural scene statistics-based no reference image quality assessment (NR IQA) algorithms employ univariate parametric distributions to capture the statistical inconsistencies of bandpass distorted image coefficients. Here, we propose a...
Underwater images often suffer from degradation such as color distortion and blurring due to light absorption and scattering. It is essential to utilize underwater image enhancement (UIE) methods to acquire high-quality images. Convolutional networks...
Pansharpening is a traditional image fusion problem where the reference image (or ground truth) is not accessible. Machine-learning-based algorithms designed for this task require an extensive optimization phase of network parameters, which must be p...
Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such c...
Infrared image colorization has gained widespread attention in recent years as an important means of enhancing image visibility and semantic expression. However, existing evaluation methods mostly rely on pixel-level differences or feature distributi...
Underwater images are important for underwater vision tasks, yet their quality often degrades during imaging, promoting the generation of Underwater Image Enhancement (UIE) algorithms. This paper proposes a Dual-Channel Convolutional Neural Network (...
Water quality and pollution is the main environmental concern for ports and adjacent coastal waters. Therefore, the development of Port Environmental Management systems often relies on water pollution monitoring. Computer vision is a powerful and ver...
Monitoring the correct operation of airport video surveillance systems is of great importance in terms of the image quality provided by the cameras. Performing this task using human resources is time-consuming and usually associated with a delay in d...
Super-resolution (SR) techniques have gained traction in biomedical imaging for their ability to enhance image quality. However, it remains unclear whether these improvements translate into better performance in clinical tasks. In this study, we prov...
Quantification of the residual tumor from early post-operative magnetic resonance imaging (MRI) is essential in follow-up and treatment planning for glioblastoma patients. Residual tumor segmentation from early post-operative MRI is particularly chal...
This study addresses the critical challenge of variable image quality in deep learning-based automated pest identification. We propose a holistic pipeline that integrates systematic Image Quality Assessment (IQA) with tailored preprocessing to enhanc...
Assessing the quality of Artificial Intelligence-Generated Content (AIGC) images remains a critical challenge, as conventional Image Quality Assessment (IQA) methods often fail to capture the semantic consistency between generated images and their te...
In recent years, significant development in the domain of dense image matching (DIM) can be observed. Meanwhile, in most countries, aerial images are acquired countrywide on a regular basis with decreasing time intervals and increasing image overlaps...
The performance of deep-learning-based segmentation models is strongly dependent on the quality of the input data, which is frequently heterogeneous or degraded in real-world medical imaging scenarios. This work presents a data-centric algorithmic pi...
To monitor objects of interest, such as wildlife and people, image-capturing devices are used to collect a large number of images with and without objects of interest. As we are recording valuable information about the behavior and activity of object...
The precise assessment of image quality that matches human perception is still a major challenge in the field of digital imaging. Digital images play a crucial role in many technological and media applications. The existing deep convolutional neural...
Image quality assessment (IQA) is a fundamental technology for image applications that can help correct low-quality images during the capture process. The ability to expand distorted images and create human visual system (HVS)-aware labels for traini...
Hyperspectral imaging (HSI), which integrates spatial and continuous spectral information, has shown considerable potential for nondestructive citrus quality assessment. However, HSI data inherently suffer from high dimensionality, strong inter-band...
Underwater images often suffer from severe color distortion, low contrast, and reduced visibility, motivating the widespread use of image enhancement as a preprocessing step for downstream computer vision tasks. However, recent studies have questione...
Explainability features are intended to provide insight into the internal mechanisms of an Artificial Intelligence (AI) device, but there is a lack of evaluation techniques for assessing the quality of provided explanations. We propose a framework to...
The intelligent recognition of formed elements in microscopic images is a research hotspot. Whether the microscopic image is clear or blurred is the key factor affecting the recognition accuracy. Microscopic images of human feces contain numerous ite...
The quality of vector images depends on a significant set of geometric and structural factors, which makes objective assessment a challenging task. This paper proposes a comprehensive approach to identifying and prioritizing these factors. Factor sel...
Location effects play a crucial role in the real estate market, encompassing aspects of accessibility and neighborhood quality. While traditional measures exist for accessibility, evaluating neighborhood quality can be a complex task. Understanding t...
An automatic quality assessment of stitched images is an essential task in image analysis and is particularly useful not only in the creation of general-purpose panoramic images but also in terrain exploration and mapping made by mobile robots and dr...
LDANet represents an innovative no-reference quality assessment model specifically engineered to evaluate colorized infrared images. This is a crucial task for various applications, and existing methods often fail to capture color-specific distortion...
Blind image quality assessment (BIQA) without reference images remains significantly challenging due to the fact that perceptual quality is largely determined by subtle, spatially localized distortions. However, existing Contrastive Language–Im...
Recent advances in artificial intelligence have significantly improved performance in medical imaging tasks such as segmentation, quantification, and report generation. However, most existing solutions operate as static pipelines with limited adaptab...
This paper introduces a novel approach for self-supervised monocular depth estimation. The model is trained on stereo–image (left–right pair) data and incorporates carefully designed perceptual image quality assessment-based loss function...
Fish iris images provide morphological information for biometric recognition and biological observation, but underwater imaging often introduces blur, reflection, occlusion, low contrast, and boundary ambiguity. Conventional no-reference image qualit...
Mango is a high-value agricultural commodity, and accurate and efficient appearance quality grading and defect inspection are critical for export-oriented markets. This study proposes a dual-pipeline deep learning framework for automated mango assess...
Stereoscopic omnidirectional images (SOIs) have gained significant attention for their immersive viewing experience by providing binocular depth with panoramic scenes. However, evaluating their visual quality remains challenging due to its unique sph...
Cereal grains are a vital part of the human diet. The appearance quality and size distribution of cereal grains play major roles as deciders or indicators of market acceptability, storage stability, and breeding. Computer vision is popular in complet...
Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement perform...
Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral...
In recent years, multimodal medical image fusion (MMIF) has attracted significant attention due to its ability to integrate complementary information from different medical imaging modalities and provide more comprehensive information for clinical an...
The estimation of image quality and noise perception still remains an important issue in various image processing applications. It has also become a hot topic in the field of photo-realistic computer graphics where noise is inherent in the calculatio...
With the increasing availability of smart devices, billions of users are currently relying on map services for many fundamental daily tasks such as obtaining directions and getting routes. It is becoming more and more important to verify the quality...
Unlike high-dose scans, low-dose cardiac CT perfusion imaging reduces patient radiation exposure and thereby the risk of potential health effects. However, it introduces significant image noise, degrading diagnostic quality and limiting clinical asse...
Freshness is a critical indicator of fruit and vegetable quality, directly affecting nutrition, taste, safety, and reducing waste across supply chains. Accurate detection is essential for quality control, supporting producers during harvesting and st...
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