UAV images are critical for applications such as large-area mapping, infrastructure inspection, and emergency response. However, in real-world flight environments, a single image is often affected by multiple degradation factors, including rain, haze, and noise, undermining downstream task performance. Current unified restoration approaches typically rely on implicit degradation representations that entangle multiple factors into a single condition, causing mutual interference among heterogeneous corrections. To this end, we propose DAME-Net, a Degradation-Aware Mixture-of-Experts Network that decouples explicit degradation perception from degradation-conditioned reconstruction for compositional UAV image restoration. Specifically, we design a Factor-wise Degradation Perception module(FDPM) to provide explicit per-factor degradation cues for the restoration stage through multi-label prediction with label-similarity-guided soft alignment, replacing implicit entangled conditions with interpretable and generalizable degradation descriptions. Moreover, we develop a Conditioned Decoupled MoE module(CDMM) that leverages these cues for stage-wise conditioning, spatial-frequency hybrid processing, and mask-constrained decoupled expert routing, enabling selective factor-specific correction while suppressing irrelevant interference. In addition, we construct the Multi-Degradation UAV Restoration benchmark (MDUR), the first large-scale UAV benchmark for compositional UAV image restoration, with 43 degradation configurations from single degradations to four-factor composites and standardized seen/unseen splits.Extensive experiments on MDUR demonstrate consistent improvements over representative unified restoration methods, with greater gains on unseen and higher-order composite degradations. Downstream experiments further validate benefits for UAV object detection.
Figures & tables
Fig. 1: Real UAV examples illustrating the impact of adverse degradations on downstream object detection. The examples include single-degradation cases and composite cases. Colored solid boxes denote detector predictions, and red dashed boxes highlight visually identifiable missed targets. Adverse degradations substantially reduce detection reliability, with more severe failures in composite cases.
Fig. 2: Motivation of DAME-Net. In blind unified restoration, multiple active degradations are often compressed into a single holistic condition, which blurs factor boundaries and limits factor-specific correction. DAME-Net instead performs explicit degradation perception and uses factor-wise cues for conditioned restoration.
Fig. 3: Motivation for FDPM. Conventional hard alignment forces a composite degradation (e.g., rain+haze) to match a single prompt, whereas FDPM preserves its overlap with constituent factors through label-similarity-guided soft alignment.
Fig. 4: Overview of DAME-Net. FDPM predicts a degradation mask m^ and an image embedding p , which are transformed by the Degradation Encoder into stage-wise conditioning vectors {gs}s=15 . CDMM then performs degradation-conditioned restoration, while the Low Frequency Predictor is supervised by Guided Filter Loss and the residual path is supervised by Spatial ℓ1 Loss and Masked Frequency ℓ1 Loss.
Fig. 5: Architecture of CDCB. The frequency branch transforms input features via FFT, applies degradation-conditioned spectral modulation with a rank- K frequency mask, and refines the DC component via content-adaptive offset prediction. The spatial branch applies Swin window attention. A learned gate w balances the two branches.
Fig. 6: Architecture of the DC-MoE. The predicted degradation mask first activates degradation-relevant experts in a Global Group and a Spatial Group, while suppressing irrelevant experts. Routing weights are then assigned only over the activated expert subset. The Spatial Group further employs a pixel-wise Spatial Router to localize expert responses within the feature map.
Setting
Ours
PromptIR [ 22 ]
DehazeFormer [ 20 ]
AirNet [ 24 ]
Restormer [ 19 ]
AdaIR [ 23 ]
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
Seen Settings
Avg. Seen Single
29.52
0.9091
29.36
0.9066
28.93
0.9002
27.40
0.8347
29.16
0.9041
28.40
0.8931
Avg. Seen Double
26.88
0.8389
26.61
0.8328
26.22
0.8207
25.07
0.7454
26.43
0.8283
25.79
0.8102
Avg. Seen Triple
25.73
0.8105
25.39
0.7988
24.95
0.7804
24.36
0.7048
25.25
0.7927
24.65
0.7681
Overall Seen
27.67
0.8602
27.43
0.8544
27.01
0.8433
25.82
0.7717
27.25
0.8504
26.57
0.8338
TABLE I: Quantitative comparison on MDUR using averaged results only (PSNR/SSIM). Results are reported for in-distribution (seen) and zero-shot (unseen) degradation settings. Best and second-best results are marked in bold and underlined , respectively.
Method
Precision ↑
Recall ↑
mAP50 ↑
mAP50-95 ↑
Δ mAP50 ↑
GT
0.5851
0.5486
0.5419
0.3211
–
Degraded
0.1530
0.1046
0.0971
0.0542
–
AdaIR
0.3189
0.2343
0.2216
0.1259
0.1245
DehazeFormer
0.3269
0.2392
0.2289
0.1307
0.1318
Restormer
0.3434
0.2505
0.2420
0.1380
0.1449
PromptIR
0.3495
0.2547
0.2469
0.1412
0.1497
TABLE II: Downstream object detection on all 43 degraded UAV settings. “Degraded” denotes detection on degraded inputs, “GT” the upper bound on clean images, and Δ mAP50 the improvement over Degraded. Best and second-best results are marked in bold and underlined , respectively.
Fig. 7: Qualitative comparison on MDUR under single and composite degradations. From left to right: Input, Restormer, PromptIR, Ours, and Ground Truth (GT). Our method better preserves scene structure and color consistency while leaving fewer visible residual degradations.
Variant
All
Quad
PSNR
SSIM
PSNR
SSIM
Full model (Ours)
23.04
0.7410
16.98
0.5211
GT Mask (oracle mask)
23.04
0.7415
16.97
0.5226
w/o Semantic Embedding
21.58
0.7055
13.67
0.4356
w/o Global Token
21.96
0.7201
14.41
0.4707
w/o Strict Token Masking
22.48
0.7320
15.96
0.5060
TABLE III: Ablation of degradation representation components. “GT Mask” replaces the predicted binary mask with the ground-truth mask while keeping the CLIP-derived semantic embedding unchanged. “All” and “Quad” denote averages over all 43 tasks and the quad-degradation subset, respectively. Best and second-best results are marked in bold and underlined , respectively.
Variant
All
Quad
PSNR
SSIM
PSNR
SSIM
Full model (Ours)
23.04
0.7410
16.98
0.5211
w/o Freq-Spa Gate
22.09
0.7203
15.22
0.4765
w/o DC-MoE
22.34
0.7222
15.48
0.4804
w/o Decouple Gate
22.46
0.7284
15.55
0.4836
w/o Freq Branch
22.28
0.7278
16.12
0.5108
TABLE IV: Ablation of restoration architecture components. “All” and “Quad” denote averages over all 43 tasks and the quad-degradation subset, respectively. Best and second-best results are marked in bold and underlined , respectively.
Variant
All
Quad
PSNR
SSIM
PSNR
SSIM
Full model (Ours)
23.04
0.7410
16.98
0.5211
w/o Freq Loss
22.32
0.7304
14.49
0.4790
w/o Mask Overload
22.58
0.7287
16.54
0.4910
w/o Guided Filter Loss
22.91
0.7452
16.88
0.5337
TABLE V: Ablation of training strategies. “All” and “Quad” denote averages over all 43 tasks and the quad-degradation subset, respectively. Best and second-best results are marked in bold and underlined , respectively.
Remote sensing images acquired by unmanned aerial vehicles (UAVs) and satellites are often degraded by adverse weather, illumination variation, and imaging artifacts, which may co-occur and jointly induce global distribution shifts and local structural corruption. Although All-in-One image restoration offers an appealing unified alternative to task-specific pipelines, existing methods still suffer from weak or implicit degradation cues and parameter redundancy caused by full-rank multi-expert designs with overlapping restoration behaviors. We propose CoRE-UIR (Common and Residual Experts for Universal Image Restoration), a prior-guided global-local framework centered on the Common-and-Residual Expert Block (CoRE). CoRE explicitly decomposes restoration capacity into a common dense expert for degradation-invariant restoration and low-rank residual experts for degradation-specific compensation, enabling adaptive specialization without redundant expert replication. Built on this design, Degradation Prior Embedding (DPE) adapts frozen CLIP features into an explicit restoration-oriented prior, while Global Feature Modulation (GFM) aligns global feature statistics before local residual compensation. We also construct MDVD-108K (Multi-Degradation VisDrone), a large-scale UAV restoration dataset covering both single and compound degradations, together with a real-world test set. Extensive experiments on multiple datasets show that CoRE-UIR improves the overall average PSNR by 1.05 dB while running 11.83× faster and reducing peak memory by 85.3% relative to the strongest baseline, BaryIR, thereby maintaining a favorable quality-efficiency trade-off. Evaluations on downstream tasks and unseen degradation also validate the generalizability of CoRE-UIR. The code and dataset will be released at https://github.com/zzaiyan/CoRE-UIR.
Zaiyan Zhang, Qiangqiang Yuan, Jie Li +5
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, Hubei, China · School of Artificial Intelligence, Wuhan University, Wuhan 430072, Hubei, China · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China
All-in-One image restoration aims to develop a unified restoration framework for handling diverse degradation types. Existing end-to-end methods usually regard the restoration process as a black-box mapping, lacking an explicit optimization interpretation. Although deep unfolding provides an interpretable iterative modeling paradigm for image restoration, existing methods mostly rely on fixed degradation assumptions or predefined degradation information, making them difficult to adapt to unified restoration requirements under complex degradations and locally damaged content. This limitation restricts their performance in degradation suppression and structural detail recovery. To address these issues, this paper proposes DVANet, a deep unfolding network inspired by the half-quadratic splitting optimization algorithm, which formulates unified image restoration under complex degradations as a collaborative unfolding process between degradation-aware observation consistency and visual-prior-guided reconstruction. Specifically, in the degradation-aware observation consistency branch, a degradation representation module is employed to extract global degradation attributes and local degradation cues, and degradation-conditioned mapping is used to enhance the model's adaptability to different degradation types. In the visual-prior-guided reconstruction branch, DINOv3 is introduced to provide structural and semantic information as hierarchical visual priors, thereby complementing the missing structural information in damaged regions and improving detail recovery. Extensive experiments demonstrate that DVANet achieves superior or competitive performance on multi-scenario degradation and cross-domain image restoration tasks, showing favorable degradation adaptability and generalization ability.
Yanjie Tu, Qingsen Yan, Axi Niu +3
School of Computer Science, Northwestern Polytechnical University, Xi’an 710000, China · Shenzhen Research Institute of Northwestern Polytechnical University, Shenzhen 518057, China · State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 999078, China
Unified image restoration using a single model often faces task interference due to diverse degradations. To address this, we propose DACG-IR (Degradation-Aware Adaptive Context Gating), which enables explicit perception of degradation characteristics to dynamically modulate feature representations. Our method constructs degradation-aware contextual representations from the input to modulate attention distribution, frequency-domain features, and feature aggregation. Specifically, a lightweight multi-scale degradation-aware module extracts coarse degradation information and generates layer-wise prompts. These prompts guide attention temperature and output gating in encoder and decoder blocks for adaptive feature extraction. Additionally, a spatial-channel dual-gated adaptive fusion mechanism refines encoder features, suppressing noise propagation from shallow to deep layers. This design effectively suppresses degradation-induced noise while preserving informative structures. Experiments show DACG-IR outperforms state-of-the-art methods in single-task, all-in-one, adverse weather removal, and composite degradation settings. Code: https://github.com/HlHomes/DACG-IR-code
Lei He, Jielei Chu, Fengmao Lv +4
the School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China · the Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu, Sichuan 611756, China · School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang, 330013, China +3