Articles tagged with Image Processing
A global study will explore structural changes in the brains of people with bipolar disorder and compare them to major depression. The project aims to develop new tools for diagnosis and treatment, leveraging unprecedented sample sizes from over 3,500 individuals with BD and 9,000 healthy controls.
A recent study published in JAMA Network Open found that heavy cannabis users exhibited reduced brain activity during working memory tasks, associated with worse performance. Abstaining from cannabis before cognitive tasks may help improve performance.
The open-source AI model analyzes medical images, generates detailed reports, and answers clinical questions to streamline diagnostics and improve accuracy. BiomedGPT aims to democratize healthcare and reduce disparities amongst patients by providing easily accessible data to bolster underserved hospitals.
Professors Scott Acton and Mathews Jacob of UVA's Charles L. Brown Department of Electrical and Computer Engineering were named to the IEEE Signal Processing Society's 2025 Class of Distinguished Lecturers for their groundbreaking work in signal processing, artificial intelligence, and medical imaging.
A team of researchers from the University of Ottawa has developed innovative methods to enhance frequency conversion of terahertz (THz) waves in graphene-based structures, unlocking new potential for faster, more efficient technologies in wireless communication and signal processing. These advancements hold great promise for wireless c...
The KAIST team developed a next-generation neuromorphic semiconductor-based integrated system that can learn and correct errors on its own. This technology will revolutionize AI use in everyday devices, making them faster, more private and energy-efficient.
A novel bio-inspired camera capable of ultra-high-speed imaging and high sensitivity has been developed by KAIST researchers. The camera mimics the visual structure of insect eyes and achieves frame rates thousands of times faster than conventional cameras, while providing clear images in low-light conditions.
A recent study has explored a new imaging approach that uses swept-source optical coherence tomography to visualize the upper airway with high precision. By integrating computational fluid dynamics, researchers were able to identify areas of turbulence and pinpoint obstruction sites, leading to more accurate diagnoses and treatment pla...
Researchers developed a non-invasive way to assess fat composition around the heart using magnetic resonance imaging. This technique may help doctors identify patients at greatest risk for cardiac problems and predict treatment outcomes.
SPACe enables researchers to analyze large datasets in an efficient way, capturing the full diversity of cellular responses. The platform provides a more nuanced understanding of how drugs interact with cells, revealing insights into mechanisms beyond cell death.
Researchers developed a new method, k* distribution method, to visualize and assess how well deep neural networks categorize related items together. The model reveals clustered, fractured or overlapping arrangements of data points, indicating accuracy and reliability issues.
A team of Rice University scientists has developed a coating that suppresses thermal emissions from hot windows while remaining transparent enough to capture thermal radiation from objects behind the window. This breakthrough enables clear thermal imaging at high temperatures, with potential applications in industries such as chemical ...
A new detection method has been used to identify over 100 small asteroids in the main asteroid belt, ranging from bus-sized to stadium-wide. This breakthrough could aid in tracking potential asteroid impactors and provide insights into meteorite origins.
Researchers developed a new image-processing method to visually clarify the internal network structure of rubber at the nanoscale. The method, which integrates knowledge of rubber material with advanced mathematical techniques, enables automatic analysis of multiple samples and confirms its reliability.
Researchers at Rice University developed soTILT3D, an innovative imaging platform that enables fast and precise 3D imaging of multiple cellular structures while controlling the extracellular environment. The platform improves upon conventional fluorescence microscopy by reducing background fluorescence and increasing imaging speed.
The inaugural workshop at Rice University's Center for Nanoscale Imaging Sciences brought together leading experts to explore advancements in cutting-edge imaging techniques. The event integrated diverse imaging modalities to uncover new insights into biological and materials systems.
Researchers introduce a new approach for megapixel-scale fluorescence microscopy through complex scattering media, resolving high-resolution images without requiring specialized equipment. This technique efficiently corrects distortions caused by light scattering, enabling clear imaging of dense targets.
Researchers introduce persistence barcodes, a tool that reduces diagnostic bias and uncovers subtle details in medical images. The method preserves key structural details, enabling clearer insights into the data.
A new computational model called Multi-Stage Residual-BCR Net (m-rBCR) uses a unique frequency representation to solve deconvolution tasks with fewer parameters and faster processing times. The model demonstrates high performance on various microscopy datasets, outperforming traditional methods.
A new crowdsourcing system, FireLoc, uses a network of low-cost mobile phones to detect wildfires minutes—even seconds—after they ignite. The system prioritizes privacy and accurately maps wilderness fires to within 180 feet of their origin.
Researchers developed a novel neural network model to reconstruct 3D digital images of relief-type cultural heritage objects from old photos. The model improves the accuracy of depth estimation and soft-edge detection, enhancing the preservation of cultural heritage.
Researchers have developed a new planar spectral singlet lens that unifies optical imaging and spectrometry, enabling simultaneous data acquisition. The device uses planar liquid crystal optics to achieve precise phase controls and spectral filtering, resulting in high-quality hyperspectral images.
Researchers at UCLA developed a new type of imaging technology that forms images in only one direction, enabling efficient and compact methods for asymmetric visual information processing and communication. The technology works exceptionally well under partially coherent light, achieving high-quality imaging with high power efficiency.
Compressed Ultra-Compact Femtosecond Photography (CUF) uses a super-dispersive metalens to capture transient events in a single image, overcoming conventional CUP technology's limitations. The system achieves ultrafast imaging at hundreds of trillions of frames per second with improved compactness and reliability.
A recent study published in Cell reveals that nearly one in six disease-causing mutations leads to proteins mislocalizing within the cell. The research team developed a high-throughput imaging platform to assess protein location and found that breakdowns in protein stability are a major driver of misplaced proteins.
Parachute fibers' behavior under stress was studied using micro-CT scans, revealing they are not isotropic and respond differently to increasing loads. The findings inform processes like parachute assembly and improve models for screening parachute materials, making industries more cost-effective and time-efficient.
Researchers introduce a novel computational holography-based method for high-resolution, non-invasive imaging through highly scattering media. The technique drastically reduces measurements required and corrects over 190,000 scattered modes using just 25 holographic frames.
WorldScribe, a new software, uses generative AI to provide real-time text and audio descriptions of surroundings for people who are blind or have low vision. The tool can adjust the level of detail based on user commands or camera frame time.
A new image processing strategy for cardiac magnetic resonance imaging identifies areas responsible for complex ventricular tachycardias, enabling preoperative planning and reducing procedure times and complications. The systematic approach eliminates operator bias and increases sensitivity for detecting these regions.
Researchers at the University of Tokyo introduce a new optical computing scheme called diffraction casting, which improves upon existing methods. The system uses light waves to perform logic operations and has shown promise in running complex calculations, including those used in machine learning.
A new microscope-integrated OCT system has been developed to identify tumor margins during brain surgery, providing high-resolution images of subsurface anatomy. The system has shown promising results in clinical studies, with the potential to improve outcomes for neurosurgery procedures.
Researchers developed a method to reconstruct hyperspectral images from standard RGB images using deep machine learning. The technique achieved over 70% accuracy in predicting soluble solid content and 88% accuracy in dry matter content in sweet potatoes, with potential applications for the agricultural industry.
Bifocal lenses with adjustable focal intensities are created by applying external voltage to bilayer liquid crystal structures. The new design enables polarization imaging and edge imaging, highlighting the outlines of objects with fine details.
A study from the University of Arkansas System Division of Agriculture has improved food quality computer predictions by using human perception data. The researchers trained a computer model to mimic human adaptation to environmental conditions, resulting in more consistent predictions under different lighting conditions.
Scientists have created an artificial compound eye that achieves real-time panoramic direct imaging and dynamic motion detection, surpassing natural compound eyes. The camera features a 180° field of view, ultrafast angular motion detection, and can be integrated into applications such as obstacle avoidance systems for drones and endos...
Researchers have introduced DSFN to improve the speed and accuracy of diagnoses of retinal disorders. This AI-powered medical imaging technique combines retina images with vascular distribution information to accurately locate the fovea in complex clinical scenarios, enabling doctors to detect early signs of ocular diseases.
A novel approach to overcome limitations of traditional methods, NeuPh uses local conditional neural fields to reconstruct high-resolution phase information from low-resolution measurements. It provides robust resolution enhancement and outperforms existing models in accuracy.
A new four-year, $459,000 grant will develop software and hardware tools to increase accessibility in STEM fields for persons with disabilities. The project aims to enhance data technologies for blind and low-vision users, making it easier for them to participate in STEM research and education.
A team of researchers developed a novel imaging system to address real-time monitoring challenges in ultrafast laser material processing. The Dual-Path Snapshot Compressive Microscopy (DP-SCM) system offers high-speed, high-resolution imaging capabilities.
Researchers developed a new two-photon fluorescence microscope that captures high-speed images of neural activity at cellular resolution, providing insights into brain function and neurological diseases. The microscope uses an adaptive sampling scheme to image neurons in real time, reducing damage to brain tissue.
A Rice University-led team is developing an affordable system to improve tumor removal accuracy for breast and head and neck cancer. The AccessPath system enables rapid, automatic tumor margin classification, revolutionizing real-time surgical guidance.
Researchers developed DeepLens design method based on curriculum learning to optimize complex lens designs. The approach considers key parameters like resolution, aperture, and field of view, providing optimal solutions without human intervention.
Cu-based NMs offer a combination of desirable characteristics, including fluorescence emission and contrast enhancement, making them valuable products of nanotechnology. These properties form the foundation for imaging-guided therapy with Cu-based NMs.
A team from Doshisha University has developed two approaches to generate adversarial examples for image cropping, achieving significant reductions in perturbation sizes. The white-box approach manipulates gaze saliency maps to produce effective images, while the black-box approach uses Bayesian optimization to target specific regions.
A groundbreaking approach inspired by bio-inspired neuromorphic imaging and speckle correlography has unveiled a revolutionary technique for optical image encryption. This method leverages computational neuromorphic imaging to encrypt images into event-stream ciphertexts, significantly enhancing security and complexity.
The study creates two 3D maps at once by combining near-infrared absorption imaging with image processing, providing insights into optimizing micro-heating and cooling devices. The technique promises to deliver new knowledge on convective plume formation at the microscale.
A new camera system called PrivacyLens can replace people in images with generic stick figures, protecting their identities and reducing unnecessary surveillance. This technology could prevent embarrassing photos from being shared online and make patients more comfortable using cameras for chronic health monitoring.
The Marine Biological Laboratory has introduced two new microscopes for biological and biomedical research, providing a valuable resource for scientists and students. The instruments enable correlative imaging, allowing researchers to confirm results in different ways, and are expected to influence further development of advanced imagi...
Researchers have developed a new 3D method for fast-moving object tracking at unprecedented speeds, with potential applications in autonomous driving, industrial inspection and security surveillance. The approach uses single-pixel imaging to calculate the object's position in real-time, reducing data storage and computational costs.
A new type of imager has been created that can capture features smaller than half the wavelength of light using solid-immersion diffractive encoding. The imager has a compact design and can directly perform quantitative phase retrieval without computer processing.
A suite of three innovations by MIT researchers allows for high-resolution, high-throughput imaging of human brain tissue at various scales. This technology pipeline enables scientists to analyze the human brain at multiple scales, potentially mapping entire brains.
Researchers at TU Wien have developed a theory to extract information from waves, allowing for precise measurements of objects in space. The theory reveals that the information content of a wave depends on its interaction with the object's properties, enabling customised waves to be generated for optimal information transfer.
Australian scientists have developed an AI-powered system to detect bushfires from space, reducing detection time by 500 times compared to traditional methods. The system uses hyperspectral imagery and onboard AI to identify fire smoke before it takes hold, allowing for faster responses and preventing loss of life and property.
Researchers developed an all-optical complex field imaging technique that captures both amplitude and phase information using intensity-based sensor arrays. This innovation simplifies the imaging process, reducing hardware footprint and energy consumption.
Researchers have developed a system combining bio-inspired cameras with AI to quickly detect obstacles around cars, using less computational power. The hybrid system detects objects up to one hundred times faster than current systems while reducing data transmission and processing needs.
Researchers at Delft University of Technology developed a drone that flies autonomously using neuromorphic image processing and control based on the workings of animal brains. The drone's deep neural network processes data up to 64 times faster and consumes three times less energy than when running on a GPU.
A new approach uses artificial intelligence to turn low-quality images into high-quality ones, enhancing the image quality of metalens cameras. This technology could make these cameras viable for intricate microscopy applications and mobile devices.
Researchers have developed new optical tweezers that can stably trap large and irregularly shaped particles using contour-tracking technology. This advancement could expand light-based trapping to a wider range of objects, including groups of cells, bacteria, and microplastics.
Engineers at Stanford University have developed a prototype augmented reality headset that uses holographic imaging to overlay full-color, 3D moving images on the lenses of regular glasses. The new approach delivers a visually satisfying 3D viewing experience in a compact and comfortable form factor suitable for all-day wear.
A new study found that GPT-4 and Google Gemini performed poorly in breast imaging classification, with a high percentage of discordant assignments that could impact patient management. The researchers emphasize the need for regulation of these large language models in high-level medical scenarios.