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 2
Measure Before You Repair: An Explainable Agent for Blind Restoration of Multi-Artifact 3-D MRI
A brain MRI scan can carry an unknown mix of artifacts, so restoration has to be blind, yet most deep-learning methods are built to correct one known artifact.Evaluating the Impact of Nonlinear Registration on Longitudinal PET Analysis in a Rodent Stroke Model
Longitudinal rodent positron emission tomography (PET) studies of stroke and neuromodulation rely on accurate registration to a common atlas space to detect group-level effects.DUNEDepth: Bronchoscopic Depth and Pose Estimation via Hybrid Adaptation of a 3D-Vision Foundation Model
State-of-the-art bronchoscopy guidance systems and computer-aided diagnosis methods have emerged as important tools for early lung cancer detection.OpenULF: A Modular Toolkit Enables Data-Driven Ultra-Low-Field MRI with both Classical and Quantum Sensors
Ultra-low-field (ULF) Magnetic Resonance Imaging (MRI) offers a portable, low-cost alternative to conventional scanners by operating at field strengths below 10 mT.Task-driven sparse-view planar X-ray computed tomography for axial metrology
Computational imaging can reduce the acquisition burden when the system is designed around task-relevant information rather than complete image recovery.Event-Based Optical Flow Constraint via Time-Sliced Plane Fitting
Event-based optical flow estimation using spatiotemporal planar approximation inherently assumes constant velocity and tends to average out temporal velocity variations.Cross-Dataset Generalization of Glaucoma Detection CNNs: Effects of Operating Threshold, Multi-Source Training, and Color Normalization
Deep learning models for glaucoma detection are usually tested on images from the same dataset used for training.Learning to read residual images for single-pixel photography
Continuous video scales in cost with camera count, resolution and recording duration, yet many monitoring tasks seek events rather than complete images and can operate on a compact single-pixel…The MASSIMAL Dataset: UAV Hyperspectral Images and Ground Truth Observations for Coastal Habitat Mapping in Norway
The MASSIMAL dataset was collected in an effort to develop new remote sensing methods for mapping shallow water coastal habitats, such as kelp forests, seagrass meadows and maerl beds.Adaptive Land Units from Earth Embeddings
Modeling land use starts with a choice of spatial units, typically a regular grid of pixels whose resolution is set by the spatial detail the process requires and, often more critically, by the…PBSR: Pseudo-Burst Super-Resolution via Zero-Shot Novel View Synthesis
Burst super-resolution (BSR) achieves higher reconstruction fidelity than single-image super-resolution (SISR) by aggregating sub-pixel information from multiple frames.With foundation-model features, aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification
Whole-slide image classifiers are usually developed within one hospital and often go untested at unseen hospitals.Bayesian PET Reconstruction with Learned Flow Matching Priors via Langevin Sampling
Image reconstruction for positron emission tomography (PET) is a challenging inverse problem due to its inherent ill-posedness and Poisson counting noise, particularly in low-dose acquisitions.PRIN: A Perceptual-Structural Retinex-Inspired Network for Low-Light Image Enhancement
Low-light image enhancement (LLIE) focuses not only on visibility recovery but also on preserving structurally meaningful content and perceptual naturalness.The Generalized Twin Image: Understanding Regularized Phase Retrieval
Image priors can dramatically improve phase retrieval reconstruction, but their effectiveness strongly varies between imaging modalities.TopoHyperClinicalGraph: A Geometry and Topology-Aware Multimodal Framework for Cancer Survival Prediction
We developed TopoHyperClinicalGraph, a multimodal framework combining hyperbolic tissue geometry, persistent topology and clinical graph learning for cancer survival prediction.Enhancing Medical Lesion Segmentation via Several Simple Modules
This work introduces three lightweight enhancement modules for medical lesion segmentation to improve clinical applicability, particularly under data-scarce and computationally constrained…IGDA-ICMH: an Importance Guided Feature and Network Dual Adaptation Framework towards Image Compression for Machine and Human Vision
With the surge of machine vision applications, image compression for both machine and human vision (ICMH) has become an important research area.GazeRIB: Systematic Evaluation of Multi-Reader Gaze-Guided Chest X-ray Learning
Radiologists' visual attention can supervise medical image learning, but how reader panel composition affects its value remains untested.Interpretable Multiple-Instance Learning of Glomerular Pathomics and Subtype Ratios for eGFR Classification
Chronic kidney disease is characterized by structural alterations in renal tissue and progressive loss of kidney function.