Results support constrained, stage-specific calibration as a practical strategy for learning from generated appearance guidance when registered aerial references are unavailable, together with more balanced exposure and color correction across diverse nighttime aerial scenes.
Abstract
Nighttime aerial image enhancement is challenged by spatially nonuniform exposure, mixed illumination, and weak structural evidence, while registered normal-light targets are difficult to capture from moving platforms. Generated normal-light images provide practical appearance guidance but may alter geometry or texture. We introduce \aeronight{}, comprising 1,500 real nighttime aerial RGB images: 1,300 inputs are associated with manually screened pseudo-references, and 200 inputs support unpaired evaluation. We propose AeroLLE, a two-stage framework that first recovers visibility with an HVI Base Enhancer and then performs Spatially Adaptive Exposure--Color Calibration (SAECC). After the Base Enhancer is selected and frozen, SAECC predicts bounded, low-resolution RGB gain and bias fields, restricting the magnitude and spatial variation of the second-stage correction. Experiments under complementary pseudo-paired and unpaired protocols demonstrate improved agreement with screened appearance targets, together with more balanced exposure and color correction across diverse nighttime aerial scenes. These results support constrained, stage-specific calibration as a practical strategy for learning from generated appearance guidance when registered aerial references are unavailable.
The proposed framework provides a robust, scalable solution for real-world illumination enhancement across diverse lighting conditions and consistently outperforms state-of-the-art supervised and unsupervised methods in terms of fidelity, perceptual quality, and generalization.
Yasmin Yasin, Muhammad Usman, Ibrahim Radwan et al.· 0 citations
In the field of unmanned surface vehicles (USV) autonomous control, fusing visible and infrared images enhances target detection robustness and supports navigation and obstacle avoidance tasks. However, infrared image acquisition is constrained by high equipment costs and harsh offshore environments, resulting in scarce samples and high acquisition costs that severely hinder the deployment of USV perception-control systems. To address this issue, this paper proposes a supervised improved Pix2Pix-based visible-to-infrared image generation method to supplement paired datasets. Three optimizations are adopted: the generator integrates cross-layer feature fusion and channel attention modules to enhance vessel structural details and feature focus; the discriminator employs a multi-scale local-global joint structure to ensure detail authenticity and overall consistency; the loss function incorporates perceptual loss under cycle consistency constraints. Experiments on a self-constructed dataset of 4000 paired vessel images show that the generated infrared images have reasonable thermal features and intact structures, with metrics significantly outperforming supervised baseline models. Ablation studies validate the effectiveness of each module, providing reliable data support for USV infrared target detection and improving the environmental adaptability of autonomous control systems.
Xiaonan Hou, Li Su, Hua Guo· 2026 IEEE International Conf...· 0 citations
An enhancement pipeline that operates entirely within classical signal processing is proposed, providing a transparent alternative to black-box machine learning methods while remaining practical on standard personal computers.
S. Bhattacharya· Asian journal of applied sci...· 0 citations
Shadows in remote sensing images obscure surface appearance and disrupt radiometric continuity, reducing the reliability of visual interpretation and downstream analysis. Remote sensing image deshadowing is an ill-posed inverse problem that requires spatially varying illumination recovery while preserving chromatic and radiometric consistency in non-shadow regions. Existing methods commonly rely on hard shadow masks for compensation or directly regress RGB intensities. Hard masks may inadequately model gradual penumbra variations and are sensitive to localization errors, often producing residual shadows or halo artifacts; direct RGB regression entangles illumination recovery with chromatic reconstruction and can introduce color casts. To this end, we propose the Boundary-Reliable Illumination-Color Interaction Network (BRIC-Net), which decouples these failures at different representation levels. A Lightness Reliability Prior (LRP) derives reliability-aware guidance from CIELAB statistics. Boundary-Adaptive Gated Mixing (BAGM) performs gated interpolation between shallow RGB and lightness features around uncertain transitions, while Spatial-Channel Mutual Modulation (SCMM) coordinates deeper spatial and channel responses for appearance-preserving illumination recovery. BRIC-Net achieves 29.46~dB full-image peak signal-to-noise ratio (PSNR) on AeroDS-Syn and 27.96~dB on SRGTA. It also obtains the lowest Perception-based Image Quality Evaluator (PIQE) scores on AISD and AeroDS-Real. Region-wise evaluations and component ablations further support its effectiveness in shadow recovery and non-shadow preservation.
Infrared imaging has unique advantages in remote sensing observation and non-contact measurement, but its inherent low contrast and blurred structural details limit the reliability of scene interpretation by human observers. Unlike deep learning-based approaches that rely on data-driven training and substantial computational resources, we propose a Hybrid Prior Enhanced Decomposition (HPED) model, a training-free, model-driven framework that incorporates structural and luminance priors into a multi-stage enhancement pipeline. An l1–l0-regularized decomposition separates the input into a base layer that preserves global structures and salient edges and a detail layer in which low-amplitude fluctuations and noise are suppressed. A prior-preserving bi-gamma correction method enhances base-layer contrast through prior-guided histogram segmentation and adaptive gray-level redistribution. An improved grayscale mapping strategy further enhances global contrast while maintaining interframe consistency. Experiments on real SWIR, MWIR, and LWIR images show that HPED ranks first among evaluated traditional and deep learning-based methods on key perceptual quality metrics (SSIM, VIF, LIF), while achieving over 25 fps on a CPU-only platform, sufficient for smooth real-time visual display. Task-oriented evaluation further shows that the HPED improves CNR and SCR by 174.7 ± 11.4% and 298.5 ± 52.1% on average over the raw input, outperforming all competing methods and suggesting potential applicability in downstream machine perception tasks such as detection and tracking.
Jie Li, Cheng Wang, Xiangyu Li et al.· Remote Sensing· 0 citations
Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs-fisheye distortion, game-rendered content, aerial views, and 360{\deg}panoramas. We present YOLOv14, a unified adaptive detection framework that addresses these variations through four complementary mechanisms, formalized under a novel Adaptive Routing and Modulation (ARM) paradigm. Unlike conventional unsupervised domain adaptation, our approach employs Target-Prior Guided Source-Domain Augmentation(TP-SDA), using only 50 unlabeled target images offline to estimate style statistics, while adversarial alignment serves as a lightweight regularizer rather than the primary adaptation driver. Together, these components enable YOLOv14 to achieve 49.1 mAP on COCO val2017 at 2.91 ms (T4 GPU), with substantial gains of +4.1 (fisheye), +6.6 (panorama), +6.4 (drone), and +26.1 (gamestylized) mAP over YOLOv12s. Crucially, we validate generalization on real-world game screenshots (GTA-V, Unity), achieving +14.2 mAP, confirming practical transferability beyond synthetic benchmarks. Code and models are released at https://github.com/zhangcbb/yolov14.
Jianzhou Lu, Jinling Jia, Jone Yawl et al.· 0 citations