Engineering & Signals · Interdisciplinary & Society
Image & Video Processing
The newest papers in Image & Video Processing that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
297 papers, from 4 September 2026 to 5 November 2026: 297 arXiv preprints.
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Newest papers, page 13
AWR-Net: Decoupling Anatomy and Appearance for 3D Fetal Brain Ultrasound Synthesis
Three-dimensional fetal brain ultrasound offers non-ionizing, cost-effective imaging with rich spatial information for comprehensive anatomical assessment, yet its development remains limited by…Compression Hurts, Pooling Helps: Information Loss in Rayleigh-Scale Estimation from B-Mode Ultrasound
Clinical B-mode images are widely available as potential data sources for quantitative ultrasound (QUS) analysis for tissue characterization.The segmentation ceiling: why explicit left-ventricular masks do not improve learned ejection-fraction regression
Accurate estimation of left ventricular ejection fraction (EF) from echocardiography is central to cardiovascular care, and deep learning enables automated EF prediction from echocardiographic video.HyperAMS-Net: Adaptive Multi-Scale Spatial Hypergraph Network for Brain Disorder Classification
Accurate classification of brain disorders from neuroimaging data remains challenging because of substantial inter-subject heterogeneity and the complex multi-scale patterns present in functional…OmniMimic: Dynamics-completed Motion Augmentation for Multi-style Omnidirectional Quadruped Locomotion
Animal demonstrations provide quadruped robots with natural and distinctive gait styles that are difficult to specify through hand-crafted rewards.CoRef-GS: Cooperative Referring Gaussian Splatting for Multi-Agent Scene Understanding
Referring scene understanding for embodied robots requires grounding object- and relation-centric language queries from a designated viewpoint.Uncertainty-driven training for three-dimensional calibrated lung nodule classification
In this work, we present an uncertainty-driven training framework for three-dimensional computed tomography (CT) lung nodule classification, where validation-based uncertainty estimates guide loss…Forecasting Intrathecal Tracer Enhancement from Pre-Contrast Brain MRI: Direct Regression versus Flow Matching
Intrathecal contrast-enhanced MRI tracks how a cerebrospinal-fluid tracer spreads through the brain, but requires repeated scans over 24--48\,h.Visual Autoregressive Priors for RAW-to-sRGB Image Signal Processing
RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect spatial alignment and missing camera metadata.Perceptual Refinement of an End-to-End Video Streaming Pipeline via Generative AI Layers
Traditional codecs treat every region of a frame alike; a generative layer can instead degrade the regions a viewer attends to least and reconstruct them at the client.Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation
Clinical decision-making for coronary intervention relies mainly on angiography and fractional flow reserve (FFR).Mammography Foundation Models for Opportunistic Prediction of Major Adverse Cardiovascular Events
Cardiovascular disease (CVD) remains the leading cause of death among women, yet cardiovascular risk assessment often relies on clinical variables that may be missing, outdated, or unavailable in…Noise2Noise Revisited: Training Pair Distributions Dominate Loss Choice in Self-Supervised Denoising
Noise2Noise (N2N) trains denoisers on pairs of independently corrupted observations, eliminating clean references.From Foundation Embeddings to Cropland Maps: Label Efficiency, Temporal Transferability and Independent Human Validation
Geospatial foundation models provide reusable representations of satellite imagery that support downstream mapping with limited task-specific modelling.Quantum-Inspired Trainable and Parameter-Efficient Tensor Networks for Image Inpainting
This work introduces quantum-inspired tensor-network circuits as trainable transforms for image inpainting.Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation
Multi-organ segmentation is often challenged by partially annotated datasets and domain shifts across different imaging sources.TAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision
We present TAPe+ML v3, a compact computer vision system based on TAPe (Theory of Active Perception), a structured representation that encodes relations among perceptual elements before recognition.Yarn tracking of large-scale 3D textile reinforcements using topological material features
Automated segmentation of CT images has become increasingly important to enhance the reliability of simulations through the generation of high fidelity numerical models.TTTIR: Unlocking Instance-Specific State Evolution via Test-Time Training for Image Restoration
Image restoration is inherently challenging due to the diverse and highly input-dependent nature of real-world degradations.Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026
BraTS-GoAT evaluates tumor segmentation across heterogeneous populations.