Degraded Infrared Small Object Detection via Degradation-Adapted Physics-Guided Restoration

Infrared small target detection (IRSTD) remains challenging due to tiny target size, low signal-to-noise ratio, severe foreground-background imbalance, and blurred boundaries in complex scenes. Existing methods usually rely on post-activation probability-domain supervision for discrimination, where weak targets and strong clutter may produce saturated and close probabilities, limiting weak-target discrimination. Meanwhile, blurred boundaries and halo-like predictions mainly stem from thermal diffusion, tiny target scale, boundary uncertainty, and insufficient explicit contour constraints. To address these issues, we propose Adaptive-Contrastive SLSIoU (AC-SLSIoU), a plug-and-play discriminative and shape-aware loss for IRSTD. Specifically, a Logit-Domain Margin Constraint (LDMC) is introduced to enlarge the response gap between targets and informative hard negatives in the logit space, thereby enhancing weak-target discrimination. Adaptive Boundary Suppression (ABS) applies scale-aware annular penalties to refine target contours and suppress halo-like overflow responses. In addition, False-Alarm Focal Loss assigns larger weights to high-probability negative samples, further penalizing persistent high-confidence false alarms. Without introducing extra inference overhead, the proposed method can be seamlessly integrated into existing detectors and consistently improves both detection accuracy and shape quality. Extensive experiments and cross-backbone evaluations demonstrate the effectiveness, robustness, and generalization ability of the proposed method for infrared small target detection.

Infrared small target detection (IRSTD) in high-resolution images is crucial for many practical applications, such as surveillance of unmanned aerial vehicles (UAVs) and UAV-based ground monitoring. However, IRSTD remains challenging due to the small size and weak features of targets, as well as significant interference from complex dynamic backgrounds. Existing detection methods often suffer from redundant computations on non-target background regions and insufficient exploitation of target context information, which limits their performance in complex backgrounds. To address these issues, we propose an efficient coarse-to-fine infrared small target detection framework with attention prior-guided knowledge distillation, termed ECFNet. In the coarse stage, we design a region binary classification network (RBCN) on grid-based multi-scale feature maps to efficiently recognize target-containing context region proposals. Moreover, we introduce a novel denoising-assisted training strategy that incorporates noisy ground-truth (GT) masks into RBCN feature maps and trains the network to reconstruct the original GT masks through a denoising task, thereby encouraging it to explicitly learn target-background context and thus better distinguish target proposals from background regions. In the fine stage, we customize a lightweight target detector to the coarse stage's region proposals for balancing accuracy and efficiency. Furthermore, we propose a knowledge distillation strategy guided by the teacher-student cross-attention prior. This mechanism directs the student to focus on critical target regions, thereby enhancing the discriminative feature representation for infrared small targets. Extensive experiments on three real infrared datasets demonstrate that our method outperforms both existing single-stage and two-stage approaches while maintaining high real-time processing efficiency.

Thermal infrared image enhancement aims to restore high-quality images from complex compound degradations. Existing all-in-one approaches typically employ a single shared backbone to handle diverse degradations, which causes gradient interference and parameter competition. To address this, we propose a Structural Entropy-Guided Decoupled (SEGD) Framework. Unlike unified modeling paradigms, SEGD decomposes compound degradations into independent sub-processes and models them in a divide-and-conquer manner through Degradation-Specific Residual Modules (DRMs). Each DRM focuses on residual estimation for a specific degradation, enabling task decoupling while remaining jointly trainable, which mitigates parameter contention. A Degradation-Aware Evidential Network further estimates degradation type and intensity, providing priors that adaptively regulate DRM restoration strength. To handle compound cases, DRMs are composed in varying orders to form multiple restoration paths, from which the most informative features are aggregated under a structural-entropy criterion, yielding decoder-ready representations with structural fidelity and degradation awareness. Integrating divide-and-conquer restoration, evidential perception, and entropy-guided adaptation, SEGD achieves fine-grained and interpretable enhancement. We also construct a nighttime TIR benchmark for evaluation under real low-light conditions. Experimental results demonstrate that SEGD surpasses state-of-the-art methods while achieving higher efficiency with fewer parameters.