arXiv:2409.13930v2 Announce Type: replace-cross 
Abstract: Computed tomography is a widely used imaging modality with applications ranging from medical imaging to material analysis. One major challenge arises from the lack of scanning information at certain angles, leading to distorted CT images with artifacts. This results in an ill-posed problem known as the Limited Angle Computed Tomography (LACT) reconstruction problem. To address this problem, we propose Residual Null-Space Diffusion Stochastic Differential Equations (RN-SDEs), which are a variant of diffusion models that characterize the diffusion process with mean-reverting (MR) stochastic differential equations. To demonstrate the generalizability of RN-SDEs, our experiments are conducted on two different LACT datasets, i.e., ChromSTEM and C4KC-KiTS. Through extensive experiments, we show that by leveraging learned Mean-Reverting SDEs as a prior and emphasizing data consistency using Range-Null Space Decomposition (RNSD) based rectification, RN-SDEs can restore high-quality images from severe degradation and achieve state-of-the-art performance in most LACT tasks. Additionally, we present a quantitative comparison of computational complexity and runtime efficiency, highlighting the superior effectiveness of our proposed approach.

تُعتبر التصوير المقطعي المحوسب (CT) تقنية تصوير حيوية، لكنها تواجه تحديات بسبب زوايا المسح المحدودة، مما يؤدي إلى صور مشوهة. تقدم هذه الدراسة معادلات تفاضلية عشوائية للتشتت في الفضاء الصفري المتبقي (RN-SDEs) لمعالجة مشكلة إعادة بناء التصوير المقطعي بزاوية محدودة (LACT). تُظهر التجارب على مجموعتين من البيانات، ChromSTEM و C4KC-KiTS، فعالية RN-SDEs في تحسين جودة الصورة من خلال الاستفادة من المعادلات التفاضلية العشوائية ذات العودة إلى المتوسط المتعلمة.

La tomografía computarizada (CT) es una técnica de imagen crucial, pero enfrenta desafíos debido a los ángulos de escaneo limitados, lo que resulta en imágenes distorsionadas. Este estudio presenta las Ecuaciones Diferenciales Estocásticas de Difusión del Espacio Nulo Residual (RN-SDEs) para abordar el problema de reconstrucción de Tomografía Computarizada de Ángulo Limitado (LACT). Los experimentos en dos conjuntos de datos, ChromSTEM y C4KC-KiTS, demuestran la efectividad de las RN-SDEs en la mejora de la calidad de imagen aprovechando ecuaciones diferenciales estocásticas de retorno a la me…

La tomographie par ordinateur (CT) est une technique d'imagerie essentielle, mais elle rencontre des défis en raison des angles de balayage limités, entraînant des images déformées. Cette étude présente les équations différentielles stochastiques de diffusion à espace nul résiduel (RN-SDEs) pour résoudre le problème de reconstruction de la tomographie à angle limité (LACT). Des expériences sur deux ensembles de données, ChromSTEM et C4KC-KiTS, démontrent l'efficacité des RN-SDEs pour améliorer la qualité des images en s'appuyant sur des équations différentielles stochastiques à retour à la moy…

Computed tomography (CT) is a crucial imaging technique used in various fields, but it faces challenges due to limited scanning angles, resulting in distorted images. This study introduces Residual Null-Space Diffusion Stochastic Differential Equations (RN-SDEs) to tackle the Limited Angle Computed Tomography (LACT) reconstruction problem. Experiments on two datasets, ChromSTEM and C4KC-KiTS, demonstrate the effectiveness of RN-SDEs in improving image quality by leveraging learned mean-reverting stochastic differential equations.

RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equations

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arXiv:2510.07492v3 Announce Type: replace 
Abstract: Computed Tomography (CT) is a vital diagnostic tool in clinical practice, yet the health risks associated with ionizing radiation cannot be overlooked. Low-dose CT (LDCT) helps mitigate radiation exposure but simultaneously leads to reduced image quality. Consequently, researchers have sought to reconstruct clear images from LDCT scans using artificial intelligence-based image enhancement techniques. However, these studies typically rely on synthetic LDCT images for algorithm training, which introduces significant domain-shift issues and limits the practical effectiveness of these algorithms in real-world scenarios. To address this challenge, we constructed a real-world paired lung dataset, referred to as Patient-uLDCT (ultra-low-dose CT), by performing multiple scans on volunteers. The radiation dose for the low-dose images in this dataset is only 2% of the normal dose, substantially lower than the conventional 25% low-dose and 10% ultra-low-dose levels. Furthermore, to resolve the anatomical misalignment between normal-dose and uLDCT images caused by respiratory motion during acquisition, we propose a novel purification strategy to construct corresponding aligned image pairs. Finally, we introduce a Frequency-domain Flow Matching model (FFM) that achieves excellent image reconstruction performance. Code is available at https://github.com/MonkeyDadLufy/flow-matching.

تعتبر الأشعة المقطعية (CT) أداة تشخيصية حيوية في الممارسة السريرية، لكن المخاطر الصحية المرتبطة بالإشعاع المؤين لا يمكن تجاهلها. تساعد الأشعة المقطعية منخفضة الجرعة (LDCT) في تقليل التعرض للإشعاع، لكنها تؤدي في الوقت نفسه إلى تقليل جودة الصورة. لذلك، سعى الباحثون إلى إعادة بناء صور واضحة من مسح LDCT باستخدام تقنيات تعزيز الصور المعتمدة على الذكاء الاصطناعي، ومع ذلك، تعتمد هذه الدراسات عادةً على صور LDCT الاصطناعية لتدريب الخوارزميات، مما يقدم مشكلات كبيرة في التحول بين المجالات.

La tomografía computarizada (CT) es esencial para el diagnóstico clínico, pero los riesgos para la salud asociados con la radiación ionizante no pueden pasarse por alto. La CT de baja dosis (LDCT) ayuda a mitigar la exposición a la radiación, pero al mismo tiempo reduce la calidad de la imagen. Los investigadores han desarrollado un marco basado en inteligencia artificial para reconstruir imágenes claras a partir de escaneos de LDCT utilizando un conjunto de datos de pulmón emparejado del mundo real llamado Patient-uLDCT, que presenta imágenes a dosis ultra-bajas al 2% de la dosis normal.

La tomographie par ordinateur (CT) est essentielle pour le diagnostic clinique, mais les risques pour la santé associés aux radiations ionisantes ne peuvent être ignorés. La CT à faible dose (LDCT) réduit l'exposition, mais compromet la qualité de l'image. Des chercheurs ont développé un cadre basé sur l'intelligence artificielle pour améliorer les images LDCT en utilisant un ensemble de données réelles de poumons appelé Patient-uLDCT, qui présente des images à dose ultra-faible à seulement 2 % de la dose normale de radiation.

Computed Tomography (CT) is essential for clinical diagnostics, but the associated ionizing radiation poses health risks. Low-dose CT (LDCT) reduces exposure but compromises image quality. Researchers have developed an artificial intelligence-based framework to enhance LDCT images using a real-world paired lung dataset called Patient-uLDCT, which features ultra-low-dose images at only 2% of the normal radiation dose.

A Denoising Framework for Real-World Ultra-Low-Dose Lung CT Images Based on an Image Purification Strategy

arXiv:2511.15406v1 Announce Type: cross 
Abstract: Reliable semantic segmentation is essential for clinical decision making, yet deep models rarely provide explicit statistical guarantees on their errors. We introduce a simple post-hoc framework that constructs confidence masks with distribution-free, image-level control of false-positive predictions. Given any pretrained segmentation model, we define a nested family of shrunken masks obtained either by increasing the score threshold or by applying morphological erosion. A labeled calibration set is used to select a single shrink parameter via conformal prediction, ensuring that, for new images that are exchangeable with the calibration data, the proportion of false positives retained in the confidence mask stays below a user-specified tolerance with high probability. The method is model-agnostic, requires no retraining, and provides finite-sample guarantees regardless of the underlying predictor. Experiments on a polyp-segmentation benchmark demonstrate target-level empirical validity. Our framework enables practical, risk-aware segmentation in settings where over-segmentation can have clinical consequences. Code at https://github.com/deel-ai-papers/conseco.

تم تقديم إطار عمل جديد للتحكم في الإيجابيات الكاذبة في تقسيم الصور، مما يعزز موثوقية التقسيم الدلالي في اتخاذ القرارات السريرية. تستخدم هذه الطريقة المستقلة عن النموذج التنبؤ المتوافق لإنشاء أقنعة ثقة تحافظ على مستوى محدد مسبقًا من الإيجابيات الكاذبة، دون الحاجة إلى إعادة التدريب. تُظهر الطريقة ضمانات عالية الاحتمالية للصور الجديدة، مما يمثل تقدمًا كبيرًا في التصوير الطبي.

Se ha introducido un nuevo marco para controlar los falsos positivos en la segmentación de imágenes, mejorando la fiabilidad de la segmentación semántica en la toma de decisiones clínicas. Este enfoque independiente del modelo utiliza la predicción conformal para crear máscaras de confianza que mantienen una tolerancia definida por el usuario para los falsos positivos, sin necesidad de reentrenamiento. El método demuestra garantías de alta probabilidad para nuevas imágenes, representando un avance significativo en la imagen médica.

Un nouveau cadre pour contrôler les faux positifs dans la segmentation d'images a été introduit, améliorant la fiabilité de la segmentation sémantique dans la prise de décision clinique. Cette approche indépendante du modèle utilise la prédiction conforme pour créer des masques de confiance qui maintiennent une tolérance définie par l'utilisateur pour les faux positifs, sans nécessiter de réentraînement. La méthode démontre des garanties de haute probabilité pour de nouvelles images, représentant une avancée significative dans l'imagerie médicale.

A new framework for controlling false positives in image segmentation has been introduced, enhancing the reliability of semantic segmentation in clinical decision-making. This model-agnostic approach utilizes conformal prediction to create confidence masks that maintain a user-defined tolerance for false positives, without requiring retraining. The method demonstrates high probability guarantees for new images, making it a significant advancement in medical imaging.

Controlling False Positives in Image Segmentation via Conformal Prediction

arXiv:2504.07503v2 Announce Type: replace 
Abstract: Event cameras asynchronously capture brightness changes with microsecond latency, offering exceptional temporal precision but suffering from severe noise and signal inconsistencies. Unlike conventional signals, events carry state information through polarities and process information through inter-event time intervals. However, existing event filters often ignore the latter, producing outputs that are sparser than the raw input and limiting the reconstruction of continuous irradiance dynamics. We propose the Event Density Flow Filter (EDFilter), a framework that models event generation as threshold-crossing probability fluxes arising from the stochastic diffusion of irradiance trajectories. EDFilter performs nonparametric, kernel-based estimation of probability flux and reconstructs the continuous event density flow using an O(1) recursive solver, enabling real-time processing. The Rotary Event Dataset (RED), featuring microsecond-resolution ground-truth irradiance flow under controlled illumination is also presented for event quality evaluation. Experiments demonstrate that EDFilter achieves high-fidelity, physically interpretable event denoising and motion reconstruction.

تقدم الورقة فلاتر تدفق كثافة الأحداث (EDFilter)، وهو إطار مبتكر لتصفية تدفقات الأحداث يستخدم تقدير تدفق الاحتمالات لتحسين معالجة بيانات كاميرات الأحداث. تتناول هذه الطريقة قيود الفلاتر الحالية من خلال نمذجة توليد الأحداث كتيارات احتمالية تتجاوز العتبات، مما يسمح بإعادة بناء تدفق كثافة الأحداث المستمر في الوقت الحقيقي. يهدف الإطار إلى تحسين دقة وكفاءة أنظمة الرؤية المعتمدة على الأحداث.

El artículo presenta el Filtro de Flujo de Densidad de Eventos (EDFilter), un marco novedoso para el filtrado de flujos de eventos que utiliza la estimación de flujo de probabilidad para mejorar el procesamiento de datos de cámaras de eventos. Este enfoque aborda las limitaciones de los filtros de eventos existentes al modelar la generación de eventos como flujos de probabilidad que cruzan umbrales, permitiendo la reconstrucción en tiempo real del flujo de densidad de eventos continuo. El marco busca mejorar la precisión y eficiencia de los sistemas de visión basados en eventos.

L'article présente le filtre de flux de densité d'événements (EDFilter), un cadre novateur pour le filtrage de flux d'événements qui utilise l'estimation de flux de probabilité pour améliorer le traitement des données des caméras d'événements. Cette approche répond aux limitations des filtres d'événements existants en modélisant la génération d'événements comme des flux de probabilité franchissant des seuils, permettant ainsi la reconstruction en temps réel du flux de densité d'événements continu. Le cadre vise à améliorer la précision et l'efficacité des systèmes de vision basés sur des événe…

The paper introduces the Event Density Flow Filter (EDFilter), a novel framework for event stream filtering that utilizes probability flux estimation to enhance the processing of event camera data. This approach addresses the limitations of existing event filters by modeling event generation as threshold-crossing probability fluxes, allowing for real-time reconstruction of continuous event density flow. The framework aims to improve the accuracy and efficiency of event-based vision systems.

Event Stream Filtering via Probability Flux Estimation

arXiv:2511.15499v1 Announce Type: new 
Abstract: Autoregressive models have recently shown great promise in visual generation by leveraging discrete token sequences akin to language modeling. However, existing approaches often suffer from inefficiency, either due to token-by-token decoding or the complexity of multi-scale representations. In this work, we introduce Expanding Autoregressive Representation (EAR), a novel generation paradigm that emulates the human visual system's center-outward perception pattern. EAR unfolds image tokens in a spiral order from the center and progressively expands outward, preserving spatial continuity and enabling efficient parallel decoding. To further enhance flexibility and speed, we propose a length-adaptive decoding strategy that dynamically adjusts the number of tokens predicted at each step. This biologically inspired design not only reduces computational cost but also improves generation quality by aligning the generation order with perceptual relevance. Extensive experiments on ImageNet demonstrate that EAR achieves state-of-the-art trade-offs between fidelity and efficiency on single-scale autoregressive models, setting a new direction for scalable and cognitively aligned autoregressive image generation.

تقدم الورقة مفهوم تمثيل التوليد التلقائي الموسع (EAR)، وهو نموذج جديد لتوليد الصور يحاكي نظام الرؤية البشري من خلال إدراكه من المركز إلى الخارج. تعمل هذه الطريقة على تحسين الكفاءة من خلال نشر رموز الصورة بترتيب حلزوني، مما يسمح بفك تشفير متوازي ويحافظ على الاستمرارية المكانية. بالإضافة إلى ذلك، يتم اقتراح استراتيجية فك تشفير قابلة للتكيف مع الطول لتعزيز المرونة والسرعة، مما يقلل من التكاليف الحسابية ويحسن جودة التوليد.

El artículo presenta la Representación Autoregresiva Expandida (EAR), un nuevo paradigma para la generación visual que imita el sistema visual humano al percibir de manera centrípeta. Este método mejora la eficiencia al desplegar tokens de imagen en un orden espiral, permitiendo la decodificación paralela y preservando la continuidad espacial. Además, se propone una estrategia de decodificación adaptativa en longitud para mejorar la flexibilidad y velocidad, reduciendo así los costos computacionales y mejorando la calidad de generación.

Cet article présente la Représentation Autoregressive Élargie (EAR), un nouveau paradigme pour la génération visuelle qui imite le système visuel humain en percevant de manière centripète. Cette méthode améliore l'efficacité en déroulant les tokens d'image dans un ordre spiralé, permettant un décodage parallèle et préservant la continuité spatiale. De plus, une stratégie de décodage adaptative en longueur est proposée pour améliorer la flexibilité et la rapidité, réduisant ainsi les coûts computationnels et améliorant la qualité de génération.

The paper introduces Expanding Autoregressive Representation (EAR), a new paradigm for visual generation that mimics the human visual system's center-outward perception. This method improves efficiency by unfolding image tokens in a spiral order, allowing for parallel decoding and preserving spatial continuity. Additionally, a length-adaptive decoding strategy is proposed to enhance flexibility and speed, ultimately reducing computational costs and improving generation quality.

RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equations

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