arXiv:2503.14012v3 Announce Type: replace 
Abstract: Remote sensing object detection (RSOD) often suffers from degradations such as low spatial resolution, sensor noise, motion blur, and adverse illumination. These factors diminish feature distinctiveness, leading to ambiguous object representations and inadequate foreground-background separation. Existing RSOD methods exhibit limitations in robust detection of low-quality objects. To address these pressing challenges, we introduce LEGNet, a lightweight backbone network featuring a novel Edge-Gaussian Aggregation (EGA) module specifically engineered to enhance feature representation derived from low-quality remote sensing images. EGA module integrates: (a) orientation-aware Scharr filters to sharpen crucial edge details often lost in low-contrast or blurred objects, and (b) Gaussian-prior-based feature refinement to suppress noise and regularize ambiguous feature responses, enhancing foreground saliency under challenging conditions. EGA module alleviates prevalent problems in reduced contrast, structural discontinuities, and ambiguous feature responses prevalent in degraded images, effectively improving model robustness while maintaining computational efficiency. Comprehensive evaluations across five benchmarks (DOTA-v1.0, v1.5, DIOR-R, FAIR1M-v1.0, and VisDrone2019) demonstrate that LEGNet achieves state-of-the-art performance, particularly in detecting low-quality objects.The code is available at https://github.com/AeroVILab-AHU/LEGNet.

طور الباحثون LEGNet، وهو شبكة خفيفة الوزن تعتمد على الحافة الغاوسية، مصممة لتحسين الكشف عن الأجسام في صور الاستشعار عن بعد ذات الجودة المنخفضة. تعالج هذه الابتكارات المشكلات الشائعة مثل انخفاض الدقة المكانية وضوضاء المستشعر التي تعيق غالبًا الكشف الدقيق. من خلال تعزيز تميز الميزات والفصل بين المقدمة والخلفية، يعد LEGNet بتقدم كبير في مجال الاستشعار عن بعد، مما يسهل التعرف على الأجسام في ظروف صعبة. قد يكون لهذا الاختراق تداعيات مهمة على تطبيقات متعددة، بما في ذلك مراقبة البيئة والاستجابة للكوارث.

Investigadores han desarrollado LEGNet, una nueva red ligera de borde gaussiano diseñada para mejorar la detección de objetos en imágenes de teledetección de baja calidad. Esta innovación aborda problemas comunes como la baja resolución espacial y el ruido del sensor que a menudo obstaculizan la detección precisa. Al mejorar la distintividad de las características y la separación entre el primer plano y el fondo, LEGNet promete avanzar significativamente en el campo de la teledetección, facilitando la identificación de objetos en condiciones desafiantes. Este avance podría tener importantes implicaciones para diversas aplicaciones, incluida la monitorización ambiental y la respuesta a desastres.

Des chercheurs ont développé LEGNet, un nouveau réseau léger à bord gaussien conçu pour améliorer la détection d'objets dans des images de télédétection de faible qualité. Cette innovation répond à des problèmes courants tels que la faible résolution spatiale et le bruit des capteurs qui entravent souvent une détection précise. En améliorant la distinctivité des caractéristiques et la séparation avant-arrière-plan, LEGNet promet d'avancer considérablement le domaine de la télédétection, facilitant ainsi l'identification d'objets dans des conditions difficiles. Cette avancée pourrait avoir des implications importantes pour diverses applications, y compris la surveillance environnementale et la réponse aux catastrophes.

Researchers have developed LEGNet, a new lightweight edge-Gaussian network designed to improve object detection in low-quality remote sensing images. This innovation addresses common issues like low spatial resolution and sensor noise that often hinder accurate detection. By enhancing feature distinctiveness and foreground-background separation, LEGNet promises to significantly advance the field of remote sensing, making it easier to identify objects in challenging conditions. This breakthrough could have important implications for various applications, including environmental monitoring and disaster response.

LEGNet: A Lightweight Edge-Gaussian Network for Low-Quality Remote Sensing Image Object Detection

arXiv:2511.19117v1 Announce Type: new 
Abstract: The miniaturization of thermal sensors for mobile platforms inherently limits their spatial resolution and textural fidelity, leading to blurry and less informative images. Existing thermal super-resolution (SR) methods can be grouped into single-image and RGB-guided approaches: the former struggles to recover fine structures from limited information, while the latter relies on accurate and laborious cross-camera calibration, which hinders practical deployment and robustness. Here, we propose 3M-TI, a calibration-free Multi-camera cross-Modality diffusion framework for Mobile Thermal Imaging. At its core, 3M-TI integrates a cross-modal self-attention module (CSM) into the diffusion UNet, replacing the original self-attention layers to adaptively align thermal and RGB features throughout the denoising process, without requiring explicit camera calibration. This design enables the diffusion network to leverage its generative prior to enhance spatial resolution, structural fidelity, and texture detail in the super-resolved thermal images. Extensive evaluations on real-world mobile thermal cameras and public benchmarks validate our superior performance, achieving state-of-the-art results in both visual quality and quantitative metrics. More importantly, the thermal images enhanced by 3M-TI lead to substantial gains in critical downstream tasks like object detection and segmentation, underscoring its practical value for robust mobile thermal perception systems. More materials: https://github.com/work-submit/3MTI.

إن إدخال 3M-TI، وهو إطار عمل متعدد الكاميرات بدون معايرة، يمثل تقدمًا كبيرًا في تكنولوجيا التصوير الحراري المحمول. تتضمن هذه الطريقة المبتكرة دمج وحدة انتباه متعددة الأبعاد في UNet الخاص بالانتشار، مما يسمح بمحاذاة الميزات الحرارية وRGB دون الحاجة إلى معايرة معقدة، مما يحسن جودة الصورة وتفاصيلها.

La introducción de 3M-TI, un marco de difusión multi-cámara sin calibración, marca un avance significativo en la tecnología de imágenes térmicas móviles. Este enfoque innovador integra un módulo de atención cruzada en el UNet de difusión, permitiendo la alineación de características térmicas y RGB sin necesidad de calibración compleja de cámaras, mejorando así la calidad y el detalle de las imágenes.

L'introduction de 3M-TI, un cadre de diffusion multi-caméras sans calibration, marque une avancée significative dans la technologie d'imagerie thermique mobile. Cette approche innovante intègre un module d'attention croisée dans le diffusion UNet, permettant l'alignement des caractéristiques thermiques et RGB sans nécessiter de calibration complexe des caméras, améliorant ainsi la qualité et le détail des images.

The introduction of 3M-TI, a calibration-free Multi-camera cross-Modality diffusion framework, marks a significant advancement in mobile thermal imaging technology. This innovative approach integrates a cross-modal self-attention module into the diffusion UNet, enabling the alignment of thermal and RGB features without the need for complex camera calibration, thus enhancing image quality and detail.

3M-TI: High-Quality Mobile Thermal Imaging via Calibration-free Multi-Camera Cross-Modal Diffusion

arXiv:2511.17895v1 Announce Type: cross 
Abstract: Spectral super-resolution (SSR) aims to reconstruct hyperspectral images (HSIs) from multispectral observations, with broad applications in remote sensing. Data-driven methods are widely used, but they often overlook physical principles, leading to unrealistic spectra, particularly in atmosphere-affected bands. To address this challenge, we propose the Spectral Super-Resolution Neural Operator (SSRNO), which incorporates atmospheric radiative transfer (ART) prior into the data-driven procedure, yielding more physically consistent predictions. The proposed SSRNO framework consists of three stages: upsampling, reconstruction, and refinement. In the upsampling stage, we leverage prior information to expand the input multispectral image, producing a physically plausible hyperspectral estimate. Subsequently, we utilize a neural operator in the reconstruction stage to learn a continuous mapping across the spectral domain. Finally, the refinement stage imposes a hard constraint on the output HSI to eliminate color distortion. The upsampling and refinement stages are implemented via the proposed guidance matrix projection (GMP) method, and the reconstruction neural operator adopts U-shaped spectral-aware convolution (SAC) layers to capture multi-scale features. Moreover, we theoretically demonstrate the optimality of the GMP method. With the neural operator and ART priors, SSRNO also achieves continuous spectral reconstruction and zero-shot extrapolation. Various experiments validate the effectiveness and generalization ability of the proposed approach.

تم تقديم مشغل الاستعادة الطيفية الفائق (SSRNO) لتحسين إعادة بناء الصور الطيفية الفائقة من الملاحظات متعددة الطيف، مما يعالج قيود الطرق المعتمدة على البيانات التي غالبًا ما تتجاهل المبادئ الفيزيائية، خاصة في النطاقات المتأثرة بالغلاف الجوي. يتضمن هذا الإطار المبتكر سابقة نقل الإشعاع الجوي، مما يضمن توقعات أكثر دقة وتوافقًا مع الفيزياء.

Se ha presentado el Spectral Super-Resolution Neural Operator (SSRNO) para mejorar la reconstrucción de imágenes hiperespectrales a partir de observaciones multispectrales, abordando las limitaciones de los métodos basados en datos que a menudo ignoran principios físicos, especialmente en bandas afectadas por la atmósfera. Este marco innovador incorpora un antecedente de transferencia radiativa atmosférica, asegurando predicciones más precisas y físicamente consistentes.

Le Spectral Super-Resolution Neural Operator (SSRNO) a été introduit pour améliorer la reconstruction d'images hyperspectrales à partir d'observations multispectrales, en répondant aux limites des méthodes basées sur les données qui négligent souvent les principes physiques, en particulier dans les bandes affectées par l'atmosphère. Ce cadre innovant intègre un antécédent de transfert radiatif atmosphérique, garantissant des prédictions plus précises et physiquement cohérentes.

The Spectral Super-Resolution Neural Operator (SSRNO) has been introduced to enhance the reconstruction of hyperspectral images from multispectral observations, addressing the limitations of existing data-driven methods that often neglect physical principles, particularly in atmosphere-affected bands. This innovative framework incorporates atmospheric radiative transfer prior, ensuring more accurate and physically consistent predictions.

LEGNet: A Lightweight Edge-Gaussian Network for Low-Quality Remote Sensing Image Object Detection

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