Articles tagged with Image Processing
This novel approach combines data-driven deep learning with polarization imaging to extract higher-dimensional features and improve imaging quality. The integration of polarization information expands the application scope of existing intensity-based deep learning algorithms.
A new approach uses a smartphone screen to create full-color 3D holographic images by leveraging computer-generated holography (CGH) and an optical component called a spatial light modulator. The method has the potential to enhance near-eye displays in virtual reality headsets, creating more realistic and interactive user experiences.
Researchers at University of Missouri are developing software that allows drones to fly independently, perceiving and interacting with their environment while achieving specific goals. This technology has the potential to assist in mapping and monitoring applications, such as 3D or 4D advanced imagery for disaster response.
Researchers developed a new tool to improve the accuracy of electronic devices that measure plant leaf color to assess health. The software uses polarization to account for variations in light and reduce errors caused by glare.
Researchers from Kobe University developed an AI image recognition algorithm that can predict mouse behavior based on brain functional imaging data, achieving 95% accuracy. The model identified critical cortical regions for behavioral classification and demonstrated near real-time speeds.
Researchers identify promising methods for early and accurate plant stress detection using AI and imaging sensor technologies. The review highlights the need for precise AI algorithms, diverse datasets, and accessible sensors like RGB cameras.
Researchers at Rice University have developed a custom-built miniaturized chemical vapor deposition (CVD) system that can observe and record the growth of 2D MoS2 crystals in real-time. Through advanced image processing and machine learning algorithms, they were able to extract valuable insights into the growth processes of these mater...
TaskMatrix.AI uses APIs to connect general-purpose foundation models with specialized models for specific tasks. The tool can perform digital and physical tasks, provide interpretable responses, and learn continuously.
A small, wearable ultrasound sticker can monitor organ stiffness and detect subtle changes that signal disease progression. The device has been shown to identify early signs of acute liver failure in rats and may one day help doctors diagnose internal organ failure more effectively.
A new depth from focus/defocus approach, DDFS, combines model-based and learning-based strategies to achieve notable improvements in performance and applicability. The proposed method outperformed state-of-the-art methods in various metrics for several image datasets.
Researchers developed a physical image denoiser using diffractive layers to process noisy input images at the speed of light, preserving desired spatial features with minimal distortions. The all-optical approach offers ultra-high speed, compact size, and low power consumption, paving the way for various inverse problems in imaging and...
A new machine learning approach combines computer vision with deep-learning algorithms to pinpoint problem areas in concrete structures. The system enables efficient identification and inspection of cracks using autonomous robots, reducing the overall inspection workload.
Researchers at UTSA's Department of Physics and Astronomy have used deconvolution algorithms to enhance images of galaxy NGC 5728 obtained by the James Webb Space Telescope. The study reveals a faint extended feature that could be part of an outflow from a supermassive black hole interacting with the host galaxy.
Researchers have developed a new approach to monitor ultrafast charge motion in strongly correlated solids, demonstrating phase transitions within femtoseconds. The technique offers sub-cycle temporal resolution and opens up new avenues for investigating ultrafast phenomena in correlated materials.
Researchers developed a carbon-based tunable metasurface absorber with an ultrawide, tunable bandwidth in the THz range. The absorber boasts high absorption efficiency and insensitivity to polarization angles, paving the way for advanced technological applications.
MIT researchers have developed a new method to track cell differentiation and study long-term processes like cancer progression or embryonic development. They used noninvasive Raman spectroscopy to monitor embryonic stem cells as they differentiated into multiple cell types over several days.
Scientists use new photography technique to observe interaction between biological cell and shock wave, revealing high-speed phenomena. The technology has potential applications in science, medicine, and industry.
A research team developed an innovative optical technique, 'spectrum shuttle,' to produce and shape GHz burst pulses. The method facilitates ultrafast imaging within subnanosecond timescales, enabling analysis of rapid phenomena.
Novel Dice loss functions, t-vMF Dice loss and Adaptive t-vMF Dice loss, have been developed to improve image segmentation accuracy in medical images. These new functions outperform conventional formulations and show great potential for critical fields like medical imaging and diagnosis.
Researchers used brain imaging and machine learning to identify distinct patterns of brain connectivity in people with autism spectrum disorder (ASD), taking into account individual differences. The study reveals that certain brain features are shared across subtypes, while others are unique to specific individuals.
Researchers at MIT have developed an alternative method to study molecular signals in cells, allowing them to track up to seven different molecules simultaneously. The technique uses fluorescent proteins that flicker on and off at different rates, enabling the tracking of specific cellular functions over time.
Researchers developed a deep convolutional neural network to pinpoint cardiac catheter tip locations in photoacoustic images, achieving high precision and recall. The approach has the potential to replace fluoroscopy during cardiac interventions, leading to safer procedures.
A team of researchers has developed a novel experimental system to simultaneously measure the mechanical properties and internal structure of rubber-like materials. The study found that strain within these materials is non-uniform, depending on the shape and size of composite particles.
Researchers developed an imaging sensor capable of detecting UV light, using it to differentiate between cancer cells and normal cells with 99% confidence. The technology leverages the unique tiered structure of butterfly photoreceptors and perovskite nanocrystals.
Researchers developed a new OCT approach to directly image coordination of tiny hair-like structures in live organisms, giving a powerful tool to investigate cilia's role in the female reproductive system. The technique revealed unexpected behaviors that contradict current views and suggested new roles for cilia.
A new method called TWC-Swin effectively restores holographic images even under low spatial coherence and arbitrary turbulence, surpassing traditional convolutional network-based methods. The study demonstrates strong generalization capabilities, extending its application to unseen scenes.
A team of researchers proposes a novel approach to generate three-dimensional holograms directly from regular 2D color images captured using ordinary cameras. This approach utilizes deep learning to transform the image into data that can be used to display a 3D scene or object as a hologram.
A novel strategy utilizing phosphorus nanolayers mitigates electrode-level heterogeneity in fast-charging lithium-ion batteries. The graphite-phosphorus composite exhibits consistent cycle retention, high Coulombic efficiency, and improved lithiation uniformity.
Osaka University researchers created a radial-coded mask that replaces conventional masks, yielding sharp images at various distances. The optimized mask design extends the depth of field, enabling better focus on both foreground and background objects.
New plant cell walls exhibit significantly different mechanical properties compared to surrounding parental walls, enabling cells to alter their local shape and influence the growth of plant organs. Researchers have discovered that new cell walls in some plants are 1.5 times stiffer than the parental cell walls.
Researchers have developed high-resolution near-eye displays with integrated light field technology, overcoming limitations of earlier displays. The new designs feature improved resolution, pixel density, and vision correction capabilities, resulting in enhanced visual comfort and immersive VR experiences.
A new study uses brain imaging to explore the neural underpinnings of uncertainty processing in OCD, finding that patients struggle with certainty and decision-making, particularly when outcomes are more certain. This understanding may offer new hope for developing therapies targeting uncertainty processing in OCD.
The Stevens INI is expanding its health disparities research with a new award to examine vascular causes of dementia in Asian Americans. The project aims to use innovative imaging techniques and clinical translations to better understand cerebral small vessel disease (CSVD) and its impact on cognitive impairment and dementia.
Researchers at Shanghai Jiao Tong University have developed a new scattering matrix method that can sculpt light output with minimal optimization time. The method offers unparalleled nonlinear scattered light control, enabling high-resolution scanning microscopy and particle trapping through dense, scattering media.
Scientists at Beijing Institute of Technology have developed an ultrafast quasi-three-dimensional technique, enabling higher dimensions to analyze ultrafast processes. This method breaks through the limitations of original observational dimensions, enhancing our ability to analyze ultra-fast processes comprehensively.
Studies reveal hyper-connectivity of VTA with visual processing regions and hypo-connectivity with cognitive control regions in patients with obesity. This imbalance is associated with food craving and cognition, but not impulsivity or hunger.
Researchers developed a neural network-based system, PAT, for snapshot compressive imaging. It achieves comparable image quality to CASSI and holds strong promise due to advancements in AI processing capabilities.
A new imaging method developed by UC researchers can identify specific lung infections in real time, reducing diagnosis time from 2-3 days to hours. The method uses injectable probes that light up under a nuclear imaging machine, enabling faster treatment for critically ill patients.
Advances in cardiac imaging enable a multifaceted cardiac evaluation, reducing cost and procedural adverse events while maintaining accuracy. This new approach allows for personalized and high-value care, reserving invasive procedures for cases requiring intervention.
Scientists at Max-Planck-Institut für Eisenforschung developed a machine learning model that enhances predictive accuracy in alloy design, uncovering new corrosion-resistant compositions. The model combines numerical and textual data, enabling the identification of optimal alloy formulas.
Researchers have developed a new measurement technique that uses the Kramers-Kronig relation to untangle complex helical light patterns from camera intensity measurements. This allows for single-shot retrieval of orbital angular momentum spectrum information, accelerating and simplifying the process compared to conventional on-axis int...
Quantum ghost imaging allows 3D imaging on a single photon level, enabling the lowest photon dose possible. The technique can be applied to image materials and tissues sensitive to light or drugs without risk of damage.
Researchers created a self-supervised AI model called GedankenNet that learns physics laws and thought experiments to reconstruct microscopic images. The model successfully reconstructed human tissue samples and Pap smears from holograms without relying on real-world experiments or data.
Researchers have discovered a way to utilize nonlinear scattering media for optical computing and machine learning. They created a novel theoretical framework involving third-order tensors, which can represent the complex relationships between input and output signals. This breakthrough has potential applications in real-world settings...
Researchers developed a new method for capturing turbulent flame behavior, providing detailed insights into flame dynamics, ignition processes and combustion efficiency. The high-speed 3D imaging approach can be used to optimize energy production processes and improve fire safety measures.
A newly developed P-VAE framework could speed up computational imaging by reducing the number of measurements required, making it suitable for applications such as scientific discovery and medical imaging. Researchers applied P-VAE to LED microscopy and computed tomography, achieving improved reconstruction with sparse measurements.
A new geometric deep learning model called GFCN has been developed to detect stroke lesions in brain imaging scans. The model leverages rich geometric information to segment brain tissue and achieves higher segmentation performance than other neural network architectures.
Researchers from Edith Cowan University developed software to analyze bone density scans, detecting abdominal aortic calcification (AAC) at high accuracy. The AI can predict cardiovascular disease events and late-life dementia, allowing for early interventions.
Researchers developed a chest imaging protocol using photon-counting CT, allowing for simultaneous evaluation of lung structure, ventilation, vasculature, and perfusion. The protocol showed advantages over standard CT, providing high image quality at lower radiation doses and better spectral resolution.
Researchers at MSU have developed a state-of-the-art imaging system that can capture brain activity in real-time, revealing the intricate processes of memory formation. The system has the potential to image 10,000 to 20,000 neurons simultaneously, offering unprecedented insights into how memories are created and recalled.
Researchers have implemented Orbital Angular Momentum (OAM) as an independent information carrier for optical holography, leading to OAM multiplexed holography. The new design approach, MHC-OAM, uses spatial light modulators to achieve multiramp helical conical beams with different parameters serving as information encryption or decryp...
Researchers developed a fast and convenient method called polarized imaging dynamic light scattering (PIDLS) to evaluate nanoparticle size, morphology and distributions. The method uses optical sphericity to describe the degree of deviation from spheres and provides statistical morphological distribution.
Engineers at Rice University and the University of Maryland developed NeuWS, a technology that can undo light scattering effects, enabling full-motion video through various media. The technology measures wavefronts to rapidly decipher phase information, overcoming the 'holy grail problem' in optical imaging.
DragGAN enables non-professionals to perform complex image edits with AI support, adjusting pose, gaze direction, and viewing angle. The method uses Generative Adversarial Networks to generate new images, promising simplified post-processing for AI-generated content.
Researchers at North Carolina State University have developed a new methodology called Patch-to-Cluster attention (PaCa) that addresses the challenges of vision transformers. PaCa improves ViT's ability to identify, classify, and segment objects in images while reducing computational demands and enhancing model interpretability.
Researchers at the Beckman Institute for Advanced Science and Technology have developed a new framework for super-resolution ultrasound using deep learning, reducing processing speeds from minutes to seconds. The new technology enables real-time blood flow visualization, overcoming challenges faced by conventional methods.
Online radiologists choose studies based on financial attractiveness, leading to delays in high-priority cases. This study found that expedited priority class contained the highest percentage of delayed studies.
Researchers develop a new composite strategy to produce clean Landsat images with reduced cloud and shadow errors. The approach uses an algorithm to select pixels from multiple dates to create a virtual median-value point, detecting and replacing clouds and shadows in the process.
Researchers developed a new probe to measure pH levels in cells, revealing a constant conversion rate from endosomes to lysosomes. The probe's ability to track pH changes enables faster diagnosis and potential treatments for lysosomal diseases.
Dr. Miaomiao Zhang's research aims to automate late mechanical activation detection from cardiac magnetic resonance images using machine learning and AI techniques. This could lead to more accurate placement of CRT electrodes and improved patient outcomes.