Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start
Authors: Fuyang Liu
Jiaqi Xu
Xiaowei Hu
Venue: NeurIPS 2025 poster ()
Keywords: Image restoration
adverse weather
deraining
dehazing
desnowing
and reinforcement learning
Primary area: applications

Abstract: Adverse weather severely impairs real-world visual perception, while existing vision models trained on synthetic data with fixed parameters struggle to generalize to complex degradations. To address this, we first construct HFLS-Weather, a physics-driven, high-fidelity dataset that simulates diverse weather phenomena, and then design a dual-level reinforcement learning framework initialized with HFLS-Weather for cold-start training. Within this framework, at the local level, weather-specific restoration models are refined through perturbation-driven image quality optimization, enabling reward-based learning without paired supervision; at the global level, a meta-controller dynamically orchestrates model selection and execution order according to scene degradation. This framework enables continuous adaptation to real-world conditions and achieves state-of-the-art performance across a wide range of adverse weather scenarios. Code is available at https://github.com/xxclfy/AgentRL-Real-Weather

Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start
Fuyang Liu', Jiaqi Xu', Xiaowei Huz- Nanjing University of Science and Technology South China University of Technology Huawei Noah's Ark Lab
Abstract
Adverse weather severely impairs real-world visual perception, while existing vision models trained on synthetic data with fixed parameters struggle to generalize complex degradations. To address this, first construct HFLS- -eethhee,,err, physics-drivens
dataset that simulates diverse weather phenomena, and then design dual-level reinforcement learning framework initialized with HFL.S Weather for cold-start training. Within this framework, at the local level, weather-specific restoration models are refined through perturbation-driven image quality optimization, enabling reward- based learning without paired supervision; the global level, meta-controller dynamically orchestrates model selection and execution order according to scene degradation. This framework enables continuous adaptation to real-world conditions and achieves state-of-the-art per- formance across a wide range of adverse weather scenarios. Code is available at https://gitubbcoo//xxc//////gg/ Real-Wehhhhe
Introduction
Adverse weather conditions present a persistent challenge for computer vision systems operating in real-world environments. Rain, snow, haze, and their interactions degrade image quality through intricate physical processes, including light scattering by atmospheric particles, dynamic sensor noise, and surface-level phenomena such as water film reflections and ice crystal refraction. Various deep-learning-base methods are developed from adverse weather image restoration, such as weather- desnowing [3]
specific models for deraining [15 61 20 68
dehazing [99
and all-in-one models for multiple weather types 27
46
35
69
63
36
58
g.
Despite these advances, existing methods often struggle in real-world applications due to funda- mental limitation: models trained on synthetic data fail to generalize effectively to umpredictable real-world degradations. This performance gap arises from three limitations in current approaches: (i) existing multi- welther synthetic datasets fail to capture the high-precision representation and the intricate physics underlying weather phenomena, (ii) conventional static models lack the capacity to adapt to novel degradation patterns encountered during real-world deployment, and (iii) single- model architectures cannot leverage dynamic coordination strategies optimally handle diverse and multiple degradation types.
To address these, develop self-evolving approach that contains the physics-driven synthetic data generation with a dual-level reinforcement learning architecture. First, we create the High-Fidelln Large-Scale Weather dataset (HFLSS -eatthrr), which simulates weather artifacts like rain, fog, and
"Corresponding author: huxiaowei @uu..edu..utnnn
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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snow based on their physical formation. This dataset contains one million images, using depth information predicted by robust depth estimation model [59 60 to generate realistic weather effects in any scene. On this foundation, we train specialized restoration models for various weather conditions, including rain, snow, haze, and mixed weather types, providing high-quality supervised cold starts. Similarly to LLMs [17]. a high-quality "cold start" is essential for the effectiveness of subsequent reinforcement learning.
Second, we design a Dual-Ievel Reinforcement Learning framework (DRL) for continuous refinement and real-world adaptation. At the local level, multiple specialized restoration models, such as derain, dehaze, and desnow, are first trained on our HFLS-Weatte dataset and then continuously refined based on real-world feedback through reinforcement learning. At the global level, a mnta-controllerr dynamically coordinates the collaboration of individual restoration models (agents) by analyzing degradation patterns and historical execution data. This dual-level synergy establishes a closed- loop learning ecosystem: at the local level, individual restoration models continuously refine their capabilitiess based on real-world feedback, while at the global level, the meta-controller dynamically optimizes model coordination for enhanced overall performance.
The key challenge for this framework is training the dual-Ievel system on real data without paired ground-truth images. Reinforcement learning offers a potential solution, as it doesn't 't require pixel- level supervision, making it suitable for scenarios where real data, particularly in adverse weather, lacks paired ground-trute images. However, unlike recent successful reinforcement learning appli- cations in large language models (e.g., Group Relative Policy Optimization. GRPO 42 where (i) multiple responses can be generated for a single prompt, enabling result comparison 17 and (ii) rule-based reward designs work well for tasks with deterministic answers 433, image restoration models typically output a fixed result for each input and it is difficult to derive deterministic rewards without paired ground-truth images.
To train each individual restoration model, we develop Perturbation-driven Image Quality Optimiza- tion (PIQO), which modifies GRPO 42 in two aspects make it suitable for image restoration tasks. First, we introduce perturbations to network parameters, enabling the model to generate different results for single input image, facilitating effective comparison during the learning process. Second, to assess performance and provide learning rewards for unlabeled real-world images, we design a reward assessment strategy for image quality, which integrates various evaluation metrics for restored images. To train the global meta-controller, we take the image quality assessment score as reward to autonomously determine the optimal execution sequence for input images and dynamically select the most suitable model to maximize performance. The scheduling policy is continuously refined through real-world interactions, enabling adaptability to changing conditions.
Lastly, we conduct various experiments under complex real-world conditions across diverse weather scenarios by comparing with various methods for removing ceather-related artifacts. The results demonstrate that our model outperforms the previous methods by a large margin, both quantitatively and visually. To our knowledge, this is the first work successfully apply GRPO concepts image restoration, demonstrating that high-quality cold start and effective reward design are key to success.
2
Related Work
2.1 Adverse Weather Image Restoration
Earlier research primarily focused on restoring images degraded by specific weather conditions, such as rain [15] 61] 20, 68 21 [54] 14], haze 19 13
44 48), and snow [32]. More recent efforts aim
to develop unified frameworks for general adverse weather removal [2 46 35
69 49
36)
All-in-One (27) first unified weather restoration via joint training; Trans Weather 46 introduced
transformer-based adaptive queries; and Chen et al. [ used contrastive learning with knowledge distillation. WeatherDiff [35] proposed diffusion-
model, while WGWS [69 employed two- stage pipeline for mmperal-oo-specife refinement. WeatherStream (64] introduced real degraded clean pairs but suffered from compression noise. Although these approaches have shown impressive results on synthetic benchmarks [15]
55 57 their real-world performance is hindered by the domain gap between controlled synthetic data and the complexity of actual environmental conditions. This gap often limits their ability to handle the unpredictable and diverse nature of real-world weather scenarios.
38
32
25
26
20

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(a) Ours
(b) Foggy Cityscapes-DSs DBF
(c) RESIDE-OOTS OTS
(d) RainCityccapss
Show100K
Figure I: Weather-degraded images from Foggy Cityscapes-DBF 40) RESIDE-OTS (25, RainCityscapes [20].
and Snow 100K 32 showcase artifacts such as ghosting and uneven weather effects resulting from depth
estimation errors. Note that RESIDE-OTS does not provide public depth maps, and Snow 100K lacks depth data.
The three rows represent clean images, depth maps, and deggaeedd images, respectively.
2.2 Large-Scale, Agent-Based, and Perturbation Methods
Recent advancements leveraged large-scale models and multi-agent systems to address image restora- tion challenges across various adverse weather conditions. DA-CLIP [33] extends CLIP [39] via dynamic controller for robust embeddings. Other methods integrate external knowledge: e.g., distilling semanties from SAM [65] 24], using prompt learning and depth priors (8) or leveraging VLMs for semi-supervised enhancement (56). RestoreAgent [6] uses multimodal LLMs to assess, sequence, and apply restoration tools autonomously. AgenticIR (67 coordinates multiple expert agents via LLMs for toolbox-baie based restoration, including synthetic degradation generation. Very recently, JarvisIR
29 also adopts multi-agent RL strategies for weather-degraded image restoration.
Some works explore perfurbattion-aase mechanisms for improving restoration diversity and robust- ness. DFPIR 45 introduces degradation-awar feature perturbation, while earlier works exploit latent-space or parameter perturbations through learned priors, such as deep mean-shift priors B and autoencoding priors [2]. Unlike these, our method employs RL-guided parameter perturbations with IQA-based reward filtering, and further introduces dual-level structure where a global meta- controller dynamically coordinates local restoration agents, enhancing adaptability real-world scenarios. Although GPT-4q shows potential in visual editing (e.g., removing weather artifacts), often produces visually appealing but physically unauthentic outputs (e.g., hallucinated objects, distorted structure) [9]. limiting its utility in tasks requiring geometric and photometrie fidelity.
3 High-Fidelity Large-Scale Weather Dataset
3.1
Dataset Overview
Existing weather-related datasets for fog, rain, and snow 41 20 21] primarily rely synthetic images generated via atmospheric scattering models [16), with depth maps sourced from LiDAR A4
20
monocular depth estimation [30]. While widely used for weather artifact removal, these datasets suffer from key limitations: (i) HIDAR-basee collection is expensive and limited in scale and scene diversity; (ii) depth maps often lack granularity, introducing unrealistic artifacts such as ghosting (see Fig. I and (iii) depth estimation models generalize poorly, further degrading realism.
To overcome these issues, develop HFLS- Weeeherrr,, large-scale, high-fidelity dataset for realistic weather synthesis. Leveraging an advanced depth prediction model [60], we generate precise and scalable simulations of rain, haze, and snow. The rain and snow simulations include both pure artifacts (i.e., rain-only or snow-only) and mixed conditions combining rain or snow with haze. This design improves both the diversity and physical plausibility of training data (Fig. TT).
W-etterr offers two core advantages: (i) High-fidelity depth scale. generate accurate depth maps from clear- weather images using a state-of-the-an model, eliminating the cost and limitations LiDAR while enabling realistic synthesis across diverse scenes. (ii) Depth- melth-weather simulation. simulaationnoo A unified framework applies depth-driven attenuation not only to haze,
3

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Table 1: Comparison of datasets for image restoration under adverse weather.
Dataset
Year
Weather
Depth
Depth Source
#Clean
#Pairs
Real/Syn
Snow 100K
32
2017
No
50,000
Syn
Rain14000
15
2017
Rain
No
650
9,100
Syn
RESIDE-OTS
25
2018
Haze
Yes
DCNF
2,061
72,135
NTURain 7
2018
Rain
No
3,123
Syo
Foggy Cityscaes
51
2018
Haze
Yes
Stereo Vision
2,975
8,925
Syn
Toggy City-DBF
Sterco Vision
Syn
RESIDE-RITS 66]
2018
Haze
4,322
Real
Rainhicac225
Syn
KtynCpisssspss 20 21
2019
Rain
Yes
Stereo Vision
262
9,432
Syn
Outdoor.Rai
26
2019
Ruin
No
13,500
CSD [0
2021
Snow
No
10,000
Syn
StatherStreum
G1
2023
RainHHaze/Snow
No
+188,000
188,000
Real (diff. time)
CDDI
2024
Rain/Huze/Snow
Yes
MegaiDepth
1,383
13,013
Syn
Weather3KK
57
2025
Rain/HazeeSnow
No
30,000
30,000
Syn
HFLSSWeather
2025
Rain Kaln/HHaze/Snow
Yes
DepthAnyyhingg v2
64
1,000,000
1,000,000
Syn
but also to rain and snow, ensuring that all weather effects decay realistically with distance. This yields physically consistent degradations aligned with scene geometry, supporting robust training for multi-weather restoration models.
3.2
Weather-Related Artifact Simulation
We simulate realistic fog, rain, snow, and hybrid conditions using an atmospheric scattering model [ [6], incorporating depth-dependent interactions between weather phenomena. real-world scenarios, distant objects often obscured by fog, while proximate regions exhibit rain streaks snowflakes 20 21]. To model this, we define the weather-affected image weeater (z) as
weather (x) J(z) (1 M(x) - F(x)) + M(x) + A(x)F(x),
where (xx represents the clean image, (xx) E [0, 1] denotes the rain/snow layer, and F(x) = (Bd(()) corresponds to the fog layer, with as the atmospheric scattering coefficient and d(x) representing high-quality depth information. A(x) is the global atmospheric light. Fog is simulated using transmission map F(x)= E BBd(x), where 3 controls fog density and d(x) provides depth information, allowing fog to naturally obscure distant objects while leaving closer objects clearer. Rain and snow are represented by the rain/snow layer (x). semi-transparent mask created through procedural generation. Rain streaks appear more intense on closer objects, while snowflakes accumulate in scattered pattern, adding realism through variable opacity based depth. In both rain and snow conditions, fog effects can be applied to objects at greater distances from the camera.
3.3 Dataset Comparison
To construct HFLS-Weather, we collected one million clean images from diverse sources including Snow 100K [32,, RESIDE-OTS (25), Google Landmark V2 [51], and OSV5M [.. Each image was randomly augmented with one weather type, i.e., haze, rain (rain-only & rain+haze), and snow (snow-only snow+haze), using our physically grounded synthesis pipeline, resulting in one million high-quality degraded images.
As summarized in Table [] HeFLh-Wetther provides balanced coverage across rain, haze, and snow, unlike prior datasets that target single weather types. Its use of high-fidelity depth enables accurate simulation of weather effects, improving realism and consistency. With one million diverse backgrounds and generated pairs, it surpasses existing datasets in both scale and diversity, facilitating robust generalization. By combining large-scale synthesis with physically consistent depth cues, HFLS- Weather addresses key limitations of prior benchmarks and supports inter-conditin learning for advanced weather artifact removal.
Dual-Level Reinforcement Learning Framework
In this work, we present dual-level reinforcement learning framework for real-world adverse weather image restoration, integrating both Perturbation-Driven Image Quality Optimization (PIQO) and Multi-Agen System to continuously refine restoration models through real-world feedback.

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Perturbation-driven Image Quality Optimization
Trained Modes
Fi Frozen Model
Porometer Update
Generating Perturbation Models
Restored images
Rewords
Morel
Poliy
Fiitered
Advantages
Input
Berturibed
Hedels
ft
A;
dlepvian Fertrrbttions
Per Huriaad
Medall
Reward Medels
Reward Fiturs
Az
Comsutation
Parturbed
Model
Fentrried
Fex
Am
Muti-Agent System
AanclptualAnnalysis and sidding
Restoration Execudion
Generating A Weather Gescription
Suczessful Restaration
Input mage
flace
Edit
Access Quility imprrsummnt
Hoze
"DP-3 Hittery success rate
Snow
Hane
T0P-2 hittory
FIA Madde's
Improved
Inx Medel
hittory
Improved
Inout Image
Tosk Flow
image
Mont-Cont
Mete- Gent-lrrrralerr
Perceptual Andlysis and Bidding + Restoration Executtion
Restored Image
Meta Controller
Perceptual Andlysis and Sidding Restoration Execution
Clear
Figure Architecture of Perturhation-Driiven Image Quality Optimization and Multi-Agent System.
Figure 2 iillusrrtes our framework, where PIQO applies Gaussian perturbations to model parameters, generating multiple restored images that are evaluated using quality assessment models to guide learning and improve adaptation to various weather conditions. The Multi-Agent System uses meta-controller to generate weather descriptions and dynamically select the most suitable restoration model based on historical success rates, thereby enhancing performance across diverse weathers.
4.1 Perturbation-drive Image Quality Optimization
While reinforcement learning has advanced L.M alignment through techniques like Group Relative Policy Optimization (GRPO) 42, its application image restoration is less explored. Unlike text generation, which naturally supports one-to-many outputs, image restoration typically follows one-to-one mapping from degraded inputs to plausible outputs, limiting diversity and complicating reward design, especially without paired ground truth. Rule-based rewards, effective in deterministic settings [33, often underperform in such underconstrained scenarios.
To address this, we present Perturbation-drive Image Quality Optimization (PIQO), GRPO-inspire framework tailored for image restoration. PIQO injects small Gaussian perturbations into model parameters during inference to produce diverse outputs for the same input. Let 0 be the current model parameters and the i-th perturbed version 0% 0 + A. For each degraded input image, we generate multiple outputs using perturbed models f(())
To evaluate output quality without paired supervision, we define a composite no-reference reward function combining four metrics: LIQE (66), CLIP-IQA 47). and Q-Align [52] The reward for the i-th output is:
T1 Wi LIQB(Í.) + wy CLIP-IQA(Í) + wa Q-Align(i.),
(I)
where w, are the weights for each metric. This reward function produces a scalar score T; for each candidate output, reflecting its predicted perceptual quality. By design, higher indicates better overall visual quality as judged by the ensemble of metrics.
Not all perturbed outputs are beneficial for learning, and some may produce degraded images with low rewards, introducing high variance even harmful gradients. To mitigate this, we apply reward filtering step that discards outputs whose rewards fall below that of the unperturbed model, ensuring the optimization focuses only advantageous directions. Let Lp denote the output from the current
5

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model and MUSIQ 23 be the filtering criterion; we retain only the indices with:
= MUSIQ(Í,) MUSIQ(I,).
Next, compute the normalized advantage A, for each retained sample i 8, which reflects how much its reward deviates from the group mean:
N
N T F)²
A F == O+
1
j=1
where F and o, denote the mean and standard deviation of the rewards ) within the retained group S. This relative advantage indicates the quality of a perturbed output compared to the group average: positive A; suggests beneficial perturbation, while negative A; implies less favorable one (note that low-quality samples have mostly been filtered out by the vp baseline). Normalizing the advantages reduces variance in the gradient estimate and serves built-in baseline, akin to standard policy gradient methods.
Given the filtered set of perturbations and their advantages, PIQO updates the model parameters in the direction that increases expected image quality. We estimate the cumulative policy gradient over the filtered subset S:
A, - -),
g
S
iES
where the negative sign reflects gradient ascent reward.
To stabilize updates, we apply implicit KL regularization by approximating divergence parameter space. This is analogous the trust-region constraint in PPO, which limits how much the policy can change single step.
KL approx
(0, 0,
(5)
[
iES
where [0] is the number of parameters and 0' the j-th parameter of the i-th perturbed model.
We compute scaling factor based KL threshold
KL approx: if upppoxx
scale
(6)
1,
otherwise
The final parameter update is:
<- 0 + 17 scale 9,
where 17 is the learning rate. This update ensures stability by preventing large shifts when parameter divergence is high.
PIQO extends GRPO to image restoration by enabling learning from unlabeled real- data through perturbation-induee diversity, reward-based filtering, and veruuede-reduced gradient updates.
4.2 Muti-Agent System
To further deal with the complex adverse weather conditions in real world, we present a multi-agent system for image restoration that can handle one or multiple adverse weather types by learning from real data. After training individual restoration models for specific weather conditions, the system utilizes specialized agents, each focusing on a particular type of degradation. These agents collaborate autonomously through a bidding mechanism driven by perceptual analysis.
As shown in Figure 2 the process begins by analyzing the input image using reta-controller (CLIP 33)), which identifies the dominant weather-relate degradation and generates a corresponding "weather description." " This semantic description guides the selection of agents for subsequent restoration stages. The system broadcasts this weather description to all registered agents, who assess the compatibility their specialization with the identified degradation. Each agent decides whether to participate in the bidding process based on its historical success rate with similar degradations.

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Table 2: Performance comparison in real-world scenarios, evaluated by IQA metrics.
Method
Snow
Haze
Rain
Q-Align
CLIP-IQA
LIQE
MUSIQ
Q-Align
CLIP-IQA
LIQE
MUSIQ
Q-Align
CLIP-IQA
LJQ
MUSIQ
Chen et
3,5898
0.4959
3.1256
60.2062
3.1109
0.3373
2,0729
$4.0597
3.7629
0.4201
2.5429
54.2367
WGWS [69
3.5901
0.5026
3.1042
60.4800
3.1137
0.3643
2.1464
54,0680
3.7986
0.4428
2,5310
$4.5487
PromptR 37
3.6492
0.5291
3.2397
61.1700
3.0906
0.3757
53.8121
3.8074
0.4466
2.5622
54.6866
OneResttore
[8
3,5884
0.5089
3.1478
61.3300
2.9825
0.3293
2,0571
53,9140
3.7019
0.4167
2,4556
55,0806
DA-CI.B
3.6261
0.5219
3.2410
61.1583
3.1301
0.3687
2.0797
54.4134
0.4656
2.5810
54.9613
Ours
3,9569
0.5918
3,9458
67.7990
3,5608
0.4561
3.0267
63.3000
4.0283
0.5623
3.2945
64.1187
Table 3: Performance comparison in real-world scenarios, evaluated by GPT-4o.
Weather
Metric
Chen el al.
WGWS
PrompilI
OneRestore
DA-CLIP DFPIR
JarvisIR
Ours
Artifact Removal *
2.949
2.664
3.057
3.116
3.047
3.570
4.421
Snow
Weather Resilience 1
3.014
3.045
3,172
3.440
3,128
3.610
4.355
Overall Visual Quality
3.012
2,936
3.232
3.464
3,003
3.730
4.393
Artifact Removal +
3.142
3.015
3.083
3.415
3,071
3.170
3.650
4.074
Huze
Weather Resilience f
3,056
2,935
3.014
3.322
3.016
3,140
3.450
4.015
Overall Visual Quality
3.070
3.098
2.978
3.415
3,211
3.240
3.580
3.948
Artifact Removal
2.965
2.978
3.371
3.359
3,275
3,340
3.710
4.254
Rain
Weather Resilience 1
2.841
2.923
3.323
3.222
3.159
3.150
3.690
4.007
Overall Visual Quality
2.984
3,014
3.314
3.201
3.163
3.180
3.870
3.896
Only agents with a high likelihood of success, based on past performance, submit bids. The system ranks the bidding agents by their historical success rates and selects the top-ranked agent handle the restoration task. Once the restoration is completed, the system evaluates the result through a two-step process: (i) The CLIP model re-analyzes the restored image to check for the targeted degradation. (ii) An objective Image Quality Assessment (IQA) score is calculated to assess the quality improvement. Specifically, we adhere to the PIQO reward configuration; see Eq. (I).
If the IQA score decreases compared to the previous round, the restoration is considered a failure. In this case, the system reverts the image its previous state, removes the failed agent from the candidate list, and selects the next highest-ranked agent for another attempt. This process continues until successful restoration is achieved three consecutive failures occur, at which point the image with the highest IQA score is returned.
If the restoration does not result in a decrease in IQA score, the system checks whether further degradation is present using the CLIP model. If degradation still detected, the restoration is considered partially successful, and the system enters the next round. The bidding process is re- initiated, excluding the agent from the previous round to avoid redundancy. If no further degradation is detected, the restoration is considered complete, and the image is returned as the final output.
To avoid computational overload, the system limits the number of agents involved in single restoration to three. If three consecutive restoration attempts result in failure (i.e., successive IQA drops), the process terminates, and the image with the highest IQA score is returned.
4.3 Training Strategy
We adopt the dual-level reinforcement learning strategy train a DSANet 112 -based multi-agent system for real-world weather adaptation, utilizing eight NVIDIA RTX 4090 GPUs. The training process begins with a cold start on the WFLL--eetherr dataset, using the Adam optimizer with a batch size of cight and learning rate of 0.0001 for up to 100 epochs. Early stopping is applied based on the validation loss. At this stage, we also fine-tune the rain sub-model on the SPA+ dataset further improve rain removal. At the local level, task- restoration agents (e.g.. deraining, dehazing, desnowing) are enhanced via citturratiiondrvvvee Image Quality Optimization (PIQO). guided by weighted image quality assessment (IQA) rewards with weights w1 = 0.2, w² = 1, and w. = 0.2. The training leverages real- world data, including 2,318 hazy images from the URHI dataset [25], and 2,433 rainy images and 2,018 snowy images from the WReal dataset (56). with learning rate of 0.0001 and a batch size of 16. At the global level, the multi-agent system further optimized using a batch size of 16 and learning rate of 0.0001.
7

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Input
Chen
WGWS
PromptIR
DA-CUP
OneRestore
Ours
Figure 3: Visual comparisons of real images under haze, snow, and rain, with
18
33
37
69
Experimental Results
To assess model performance in removing weather artifacts, we use the WReal dataset [56]. which
includes 4,322 real haze images from RTTS [25), 2,320 real rain images from DDN-SIRR (50 and
Real3000 31 (excluding synthetic scenes), and 1,329 real snow images from Snow 100K 32
5.1 Performance Comparison with State-of-the-Arl Methods
Quantitative comparison. In real-world scenarios, the lack of labeled images affected by haze, snow, or rain complicates the evaluation of image restoration methods. Metrics such as CLIP- IQA 47 and Q-Align [53], originally designed for general datasets, often fail to capture subtle noise and residual artifacts, resulting in inflated scores that misrepresent visual quality. To address these issues, besides IQA metrics, we use GPT-4o 34 for additional evaluation across artifact removal, weather resilience, and overall visual quality. Artifact removal assesses artifact suppression and color accuracy, while weather resilience evaluates model robustness in more challenging cases. Overall visual quality measures the aesthetic coherence the image.
Table 2 and Table 3 show the results of common image quality metrics and GPT-4o evaluation, comparing with recent image restoration methods under adverse weather conditions, including Chen et al. [II] WGWS[69), PromptIR OneRestore [18) DA-CLIP (33, DFPIR [5], and JarvisIR [29]. From the results, we observe the following: (i) Our method outperforms all competitors under snow, huze, and rain, demonstrating strong generalization across diverse real-worke degradations. (ii) Our method achieves the highest scores all IQA metrics, reflecting superior visual fidelity and semantic relevance. (iii GPT-4o's 's perceptual evaluation highlights method's excellence in artifact removal,

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Table 4: Comparison of models that pre-trained various synthetic and real datasets as different "cold starts", followed by further finetuning with our PIQO approach.
Metric
Snow
Huze
Rain
RealSnow Snow 100K
Our Snow Our Haze
OTS
ITS
Our Haze
SPA+
Rain 1300
Our Roin Our Rain+Haze
Q-Align 3.6974
3.7490
3.8482
3,8693
3.1220
3.5329
3.7805
3.6974
3.9318
3.92US
CLIP-IQA
0,5315
0.5149
0.5653
0.5625
0.3846
0.3697
0.449
0.4612
0.4205
0.5340
0,5615
LIQE
3.3386
3,4609
3.7094
3.7741
2.1239
2.1412
3,0120
2.6766
2.5681
2,9805
3.0485
MUSIQ
63.5571
64,2048
69.86
69.86
54,8760
54.5783
63.6006
56.5147
55.2493
60.4364
61.8700
Input
basic
besic-Agent
basic
Dasic+PiQOAApen
Figure 4: Visual ablation study of the framework components.
weather resilience, and overall visual quality. (iv) While competing methods fluctuate across different weather conditions, approach maintains stable and superior performance, demonstrating that real-world refinement via dual-level reinforcement learning significantly boosts generalization and robustness beyond the models trained solely synthetic data.
Visual comparison. Figure 3 shows the visual comparison results, where the effectiveness of our approach is demonstrated across different weather conditions, including haze, snow, and rain. As shown, our method consistently delivers superior restoration quality, better preserving fine details, colors. and structural integrity of the scene, while others may fail to remove weather degradations, recover important features introduce artifacts, particularly in challenging real weather conditions.
Latency comparison. Our Multi-Agent System incurs higher latency (570 ms) due to running multiple specialized models, yet it achieves superior restoration quality under complex weather. Compared with single-model baselines such as OneRestore (17 ms), Chen et al. (18 ms), WGWS (95 ms), and PrompuR (208 ms), framework is slower but more robust. At the same time, remains far more efficient than other multi-agent systems like DA-CLIP (6543 ms) and JarvisIR (15250 ms), striking favorable balance between efficiency and performance.
5.2 Ablation Study
Evaluation on the "cold starts." To evaluate the effectiveness of our high-fidelity synthetic dataset for cold-start pretraining, we conduct studies both single-degradation and mixed-weather datasets, comparing models initialized on different synthetic datasets and then refined using our PIQO.
As the results in 4 have the following observations. (i) Superiority of Fiigh-Quaity Cold Start: Models initialized with HFLS-Weather Weather show significant improvements in image quality over those trained prior public datasets for snow, haze, and rain. (ii) Cross- Weather Pretraining Helps: Incorporating weather diversity in pretraining, such as combining snow and haze, enhances performance, suggesting that exposure multiple degradation types improves generalization during PIQO finetuning. Additional comparisons of HFLLS- S-Weather are in the Appendix.
Evaluation of the framework design. To assess the contribution of each component, we conduct ablation studies under real-world weather conditions using three quantitative metrics: CLIP-IQA. LIQE, and Q-Align. The Basic model refers to the baseline image restoration network pretrained on our HFLS- S-eathherr synthetic dataset, while Agent denotes the proposed multi-agent coordination system. As shown in Table and Figure 4 (i) adding the PIQO training significantly enhances per- ceptual quality, especially under challenging conditions like rain and snow; (ii) the Agent framework improves adaptive restoration by dynamically dispatching specialized agents; and (iii) combining PIQO with Agent yields the best performance across all metrics, highlighting the effectiveness of joint local optimization and global coordination.

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Table 5: Ablation study of the framework components under snow, haze, and rain.
Method
Snow Q-Align
Haze Q-Align
Rain
CLIP-IQA
LIQE
CLIP-IQA
Q-Align
CLIP-IQA
Q-Align
Q-Aliga
Basic
0.4774
3.6649
3.1794
0.3661
3.2673
2.0232
0,4392
3.7678
2.5349
Basic + Agent
0.5242
3.7415
3.4893
0.3814
3.2977
2.2302
0.4721
3,8785
2,6871
Basic PIQO
0.5653
3.8482
3.7094
3.5329
3.0120
0.5340
Basic PIQO Agent
0,5918
3,9458
3,9569
0,4561
3,5608
3.0267
0,5623
4,0283
3.2945
Conclusion
We develop dual-level reinforcement learning framework for real-world adverse weather image restoration, combining a physics-driven synthetic dataset (HFLS- ---eather) with a two-tier adaptive learning system. At the local level, weather-specifie models are refined using perturbation-driven optimization without paired supervision. At the global level, a meta-controller dynamically schedules model execution based degradation patterns. Nevertheless, the multi-agent system introduces extra inference-time overhead as a result of its multi-round interactions.
Potential negative societal impacts. While our method improves visual robustness in adverse conditions, it may be misused for surveillance or deepfake generation, and poses risks in safety-crition applications without proper validation. Responsible use and safeguards are necessary.
Acknowledgment
This work was supported by the Research Start-up Fund for Prof. Xiaowei Hu at the Guangzhou International Campus, South China University of Technology (Grant No. K3250310).
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20

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A Additional Experimental Results
A.1 Training on Different Synthetic Datasets
Table below compares the performance of models trained on different synthetic datasets for snow, haze, and rain conditions, using metrics such as Q-Align [53], CLIP-IQA [47], LIQE [66], and MUSIQ [23]. For snow, models trained on "Our Snow" and "Our Snow+Haze" datasets outperform others, with "Our Snow+Haze" achieving the highest Q-Align score of 3.7179 and the best CLIP-IQA score of 0.4964, indicating superior quality and alignment. Haze models also favor "Our Haze," which scores highest in Q-Align (3.2673) and LIQE (2.2555), suggesting better restoration of haze-affected images compared to others like OTS and ITS. In the rain category. the "Our Rain+ model excels across all metrics, particularly Q-Align (3.8148) and MUSIQ (56.1142), outperforming models trained on Rain 13k and "Our Rain."
These results highlight the benefits of combining multiple weather conditions (rain and haze) in training, as it leads to more realistic and effective restoration. Overall, our dataset consistently outperforms other synthetic datasets, proving the effectiveness of HeLt-Weather in generating realistic and high-fidelity weather conditions for image restoration tasks.
Table 6: Performance comparison of models trained different synthetic datasets.
Metric
Snow
Haze
Rain
|RealSnow (W]]
Snow DOO
Our Snow Our Snow+Have OTS
ITSES
Our Haze [SPA+
Raint 33 22
Our Rain Our Rain-Haze
Q-Align CLIP-IQA
3.6037
3.6357
3.6649
3.7179
3,0942
3.0309
3.2673
3.7707
3.6357
3,7678
3.8148
LIQE 3,0512
3,2642
3.1794
3.3741
2,0663
2.0232
2,2555
2,4695
2.3275
2,5349
2.5779
MUSIQ
61,3510
61.6517
62.3326
62.3262
53.9367
53.9367
55,8514
55.1913
53.6375
55.4581
56.1142
A.2 Re-training Other Methods on the HFL.SWeather
potential concern that performance improvements may primarily come from the scale of the dataset rather than the proposed framework. To clarify this, we first report in the main paper (Table 5) the results of our model trained solely on HFL.S Weather without reinforcement learning (PIQO) or the agent framework, which already demonstrates the additional benefit of our algorithmic components.
To further disentangle the contributions, we re-trained two representative baselines, WGWS (CVPR 2023) and OneRestore (ECCV 2024), on HFLS- Seeehhe using their released code. The quantitative results are presented in Table Both baselines improve notably when trained on HFLS- Weather, confirming that the dataset provides strong supervision. Nevertheless, our full framework consistently outperforms them across all metrics and weather conditions, indicating that dataset scale alone does not account for the gains.
These results demonstrate two key points: (i) HFLS- S-Weather indeed a valuable contribution that benefits existing methods, and (ii) the proposed PIQO and dual-level agent framework provide substantial additional improvements, establishing state-of-the-art performance under diverse weather degradations.
Table 7: Performance of re-trained baselines on Weather. Metries include LIQE, CLIP-IQA, Q-Align, and MUSIQ.
Snow
Haze
Rain
Method
LJQE
CLIP
Q-Align
MUSIQ
IJQE
CL.IP
Q-Align
MUSIQ
L.QQE
CLIP
Q-Align MUSIQ
WGWS
3.71
0.54
3.40
65.67
3.31
0.40
2.57
56.76
3.81
0.47
2.70
56.62
OneRestore
3.73
0.55
3.56
65.32
3.38
0.41
2.67
57.13
3.83
0.48
2.68
58.67
Ours
3.84
0.56
3.70
66,30
3.53
0.44
3.01
63.60
3.93
0.53
2.98
60.43
B More Comparisons with the State-of-the-Art Methods
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(a) Input
(b) Chen
(c) WGWS
(d) PromptIR
500
(c) DACLIP
(1) One
(g) Ours
Figure S: Visual comparison of real-world images under snow with
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(a) Input
(b) Chen et al.
(c) WGWS
(d) PromptIR
(c) DACLIP
(1) One
(g) Ours
Figure 6: Visual comparison of real-world images under snow with
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PT
RPI
(a) Input
(h) Chen
RP
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(d) PromptIR
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(c) DACLIP
(1) OneRest
(g) Ours
Figure 7: Visual comparison of real-world images under snow with
L
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Figure 8: Visual comparison of real-world images under snow with
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Figure 9: Visual comparison of real-world images under snow with
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Figure 10: Visual comparison of real-world images under snow with
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WGWS
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Figure 11: Visual comparison real-world images under snow
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Restore
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Figure 12: Visual comparison of wolld images under with
B
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Figure 13: Visual comparison of real-world images under snow with
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Figure 14: Visual comparison of real-world images under snow with
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Figure 15: Visual comparison of real-world images under snow with
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Figure 16: Visual comparison of real-world images under snow with
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G
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Figure 17: Visual comparison of world images under snow with
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Figure 18: Visual comparison of real-world images under snow with
1
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(b) Chen et al.
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(d) PromptIR
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(g) Ours
Figure 19: Visual comparison of real-world images under haze with
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(d) PromptIR
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(g) Ours
Figure 20: Visual comparison of real-world images under haze with
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Figure 21: Visual comparison of real- world images under haze with
33 S
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Figure 22: Visual comparison of real-world images under haze with
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Figure 23: Visual comparison of real-world images under haze with
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Figure 24: Visual comparison of real-world images under haze with
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USSS
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(f) OneRestore
(g) Ours
Figure 25: Visual comparison of real-world images under haze with
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(g) Ours
Figure 26: Visual comparison of real-world images under baze with
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Figure 27: Visual comparison of real-wort images under haze with
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Figure 28: Visual comparison of real- world images under haze with
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OneRest
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Figure Visual comparison world images under haze with
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(g) Surrs
Figure 30: Visual comparison of real-world images under haze with
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TAXI
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TAXI
TAXI
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EOR
Bon
(g) Ours
Figure 31: Visual comparison of real-world images under haze with
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Figure 32: Visual comparison of real world images under haze with
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Figure 33: Visual comparison of real-world images under haze with
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Figure 34: Visual comparison of real-world images under haze with
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AD OTE2
(a) Input
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oTEO
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Figure Visual comparison real-world images under haze with
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Figure 36: Visual comparison of real-world images under rain with
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aa) Input
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Figure 37: Visual comparison of real-world images under rain with
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Figure 38: Visual comparison of roal-world images under rain with
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Onc
(g) Ours
Figure 39: Visual comparison of real-world images under rain with
I8
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AE
(a) Iaput
(b) Chen
(c) WGWS
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NE
(c) DACLIP
OneRestore
(g) Ours
Figure 40: Visual comparison of real-world images under rain with
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(b) Chen
XBC
(c) WGWS
(d) PromptIR
ARCS
KBCS
(c) DACLIP
ResRestore
(g) Ours
Figure 41: Visual comparison of real-world images under rain with
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Figure 42: Visual comparison of real-world images under rain with
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Figure 43: Visual comparison of real- world images under rain with II
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Figure 44: Visual comparison of real-world images under rain with
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Figure 45: Visual comparison of real-world images under rain with
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Figure 46: Visual comparison of real-world images under rain with
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Figure 47: Visual comparison of real-world images under rain with
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Figure 48: Visual comparison of real-world images under rain with
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C Additional Results Produced by Our Method
We provide more qualitative results under snow, haze, and rain conditions.
66

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2
2
Input
Ours
Input
Ours
Input
Ours
Input
Ours
Figure 49; Additional results produced by our method (snow, set 1).
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Figure 50: Additional results produced by our method (snow, set 2).
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Figure 51: Additional results produced by our method (snow, set 3).
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RERUIT
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70
Figure 52: Additional results produced by our method (haze, set 1).

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Input
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Figure 53: Additional results produced by our method (haze, set 2).
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IPLE
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Figure 54: Additional results produced by method (haze, set 3).
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Figure 55: Additional results produced by our method (rain, set 1).
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Figure 56: Additional results produced by our method (rain, set 2).
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Figure 57: Additional results produced hy our method (rain, set 3).
75


Reviews:
[{"review_id": "rl8N7FxoZA", "reviewer": "Reviewer_YKa4", "summary": "This paper proposes a dual-level reinforcement learning (RL) framework for real-world adverse weather image restoration. It introduces a synthetic dataset (HFLS-Weather) for cold start and learns to adapt to real-world conditions using a two-level control mechanism: a Perturbation-based IQA Optimization (PIQO) module and a global meta-controller for multi-agent scheduling. The method is evaluated on real-world datasets and achieves superior performance compared to several baselines.", "questions": "How does the proposed method compare quantitatively and qualitatively with JarvisIR or other recent multi-agent RL-based restoration approaches?\n\nCould you report inference time and computational overhead compared to single-model baselines? This is crucial to judge practical utility.\n\nHave you tested the method on real-world datasets that are not augmented with HFLS-Weather synthetic pretraining? How robust is the model without the cold start?", "limitations": "The authors did not clearly address efficiency trade-offs of their method. A framework involving multiple agents and meta-controllers may be difficult to deploy in latency-sensitive applications such as autonomous driving. Moreover, the use of synthetic data for initialization could still introduce bias, and the generalization ability to unseen degradation types should be further evaluated. I suggest the authors add:\n\nA detailed runtime and model size analysis.\n\nA discussion of potential performance degradation in non-synthetic test domains.\n\nClarification on whether the agent decisions are interpretable or human-understandable.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\nThe proposed dual-level RL design is well-motivated and provides a practical solution to the unpaired restoration challenge under real-world adverse weather.\n\nThe integration of no-reference IQA metrics (LIQE, CLIP-IQA, QAlign) for reward modeling enables the framework to work in a self-supervised fashion.\n\nThe synthetic dataset (HFLS-Weather) shows strong fidelity and diverse degradation simulation, which helps reduce domain gap.\n\nExperiments are extensive, covering multiple weather conditions, and are supported with both objective metrics and perceptual evaluations.\n\nWeaknesses:\n\nRelated work discussion is incomplete: The paper omits prior works that adopt a similar RL-based image restoration paradigm. In particular, JarvisIR (CVPR 2025) also employs a multi-agent RL strategy to improve image quality under adverse weather, guided by perception-aware rewards. While this submission differs in its use of IQA metrics, the high-level structure and adaptive policy scheduling are conceptually similar and should be discussed.\n\nAlgorithmic novelty is somewhat limited: The core method builds upon existing mechanisms like parameter perturbation and hierarchical controllers (inspired by GRPO and meta-RL) without introducing fundamentally new learning algorithms.\n\nEfficiency concerns: The proposed framework adds significant inference overhead due to the use of multiple agents and iterative optimization, but runtime and memory profiles are not reported.\n\nWriting quality: The abstract contains a duplicated sentence, and several grammatical errors appear throughout the paper. Some figures (e.g., Fig. 2) are visually dense and lack clear annotation, which hinders readability.", "was_revised": true, "final_justification": "The authors’ response has addressed most of my concerns. I look forward to seeing a more thorough discussion of related work in the revised version. The contributions of the paper are acceptable, and I am raising my score accordingly.", "rebuttal": "We sincerely thank the reviewer for the valuable comments and constructive suggestions, which have played an important role in helping us further improve our work. Your review has been extremely helpful in refining our thinking and enhancing the quality of the paper. We also sincerely appreciate your recognition of the good motivation and effectiveness of our work. Below, we provide detailed responses to your comments, with related issues grouped together for clarity. All corresponding discussions and additional experimental results will be included in the final camera-ready version.\n\n---\n**[W1] Related works:**\n\nThank the review for pointing out the work of JarvisIR (CVPR 2025). We will include it in the revised paper. While both JarvisIR and our method adopt multi-agent RL strategies for weather-degraded image restoration, our approach differs in two key aspects: (i) we design a dual-level framework where a global meta-controller dynamically schedules restoration agents, complementing the local PIQO-based refinement, and (ii) we introduce a composite, no-reference reward function based on IQA metrics (CLIP-IQA, LIQE, Q-Align) with reward filtering to improve learning stability. \n\n---\n**[W2] Novelty:**\n\nWe propose PIQO, a novel approach that successfully extends GRPO-style reinforcement learning to image restoration by addressing the challenges of single-image input and non-deterministic rewards. The overall dual-level reinforcement learning framework for adverse weather image restoration is another contribution.\nThe novelty of our method is acknowledged by *Reviewer KNba-S2*, *Reviewer oVun-S1*, and *Reviewer jNge-S2*, such as “The proposed two-level reinforcement-learning strategy (PIQO and meta-agent) is novel and effective for real-world weather restoration.”\n\n---\n**[W3&Q2] Efficiency:**\n\nWe report that our full framework (PIQO + Agent) takes on average 0.57 seconds per image on an RTX 4090 GPU with 17 GB peak memory usage. We also provide an ablation with a lightweight single-agent PIQO variant (0.027 seconds, 4.3 GB) that balances performance and efficiency. We will include these details and discuss practical deployment trade-offs.\nIt is worth noting that our method significantly outperforms JarvisIR in both inference speed and resource efficiency. Our method takes on average 570 ms and 17 GB peak memory per image, while JarvisIR requires 15,250 ms and 29.5 GB peak memory.\n\n---\n**[W4] Writing:**\n\nThanks for the careful review. We have removed the duplicated sentence in the abstract, corrected grammatical issues throughout the paper, and revised Figure 2 with clearer annotations and a simplified layout for better readability in the revised paper.\n\n---\n**Q1:**\nWe perform the experiments with JarvisIR and DFPIR (CVPR 2025) on WReaL data and show the results below. The performance is evaluated by GPT-4o; see paper for details.\nNotably, our method demonstrates superior performance compared to these strong competitors.\n\n| Weather | Metric             | DFPIR | JarvisIR | Ours  |\n|---------|--------------------|-------|----------|-------|\n| **Snow** | Artifact Removal    | -     | 3.57     | 4.42  |\n|         | Weather Resilience  | -     | 3.61     | 4.35  |\n|         | Overall Visual Quality | -   | 3.73     | 4.39  |\n| **Haze** | Artifact Removal    | 3.17  | 3.65     | 4.07  |\n|         | Weather Resilience  | 3.14  | 3.45     | 4.02  |\n|         | Overall Visual Quality | 3.24 | 3.58     | 3.95  |\n| **Rain** | Artifact Removal    | 3.34  | 3.71     | 4.25  |\n|         | Weather Resilience  | 3.15  | 3.69     | 4.07  |\n|         | Overall Visual Quality | 3.18 | 3.87     | 3.90  |\n\n---\n**Q3:**\nThank you for the question. Yes, we have tested our method on real-world datasets without the HFLS-Weather synthetic pretraining (i.e., without the cold start). In these tests, we observed that the model struggles to converge when mitigating weather-induced degradations, showing inferior performance compared to the cold-start approach. Our ablation study (Table 4 in paper) highlights the superiority of HFLS-Weather as a cold start, showing substantial improvements in image quality for snow, haze, and rain. Additionally, pretraining with diverse weather types, such as combining snow and haze, enhances the model's generalization ability during fine-tuning with PIQO.\n\n---\n**Limitation 1:**\nWhile our Multi-Agent System involves running multiple specialized models, which increases latency (570ms), it offers better restoration quality for complex or mixed weather conditions compared to single-model methods like OneRestore (13ms). Though slower than single-model systems, it is more efficient than other multi-agent methods like JarvisIR (15250ms). We will include a detailed efficiency-performance comparison in the revision to highlight these trade-offs. Please also see W3 for model size.\n\n| Methods     | Latency  |\n|-------------|----------|\n| **WGWS**    | 68ms     |\n| **Chen et al** | 33ms  |\n| **PromptIR** | 151ms  |\n| **DACLIP**   | 6543ms  |\n| **OneRestore** | 13ms  |\n| **JarvisIR** | 15250ms  |\n| **Ours**     | 570ms   |\n\n---\n**Limitation 2:**\nTo address the potential performance degradation when a model trained on synthetic datasets is tested in real, non-synthetic environments, we propose the Perturbation-driven Image Quality Optimization (PIQO) reinforcement learning method. As demonstrated in Table 5 and Figure 4, PIQO facilitates continuous optimization by fine-tuning the model in real-world scenarios. This adaptive process significantly enhances performance, enabling the model to better handle real-world degradations and improving its robustness in actual environments.\n\n---\n**Limitation 3:**\nEach agent's decision is based on its specialization in a particular degradation type (e.g., snow, haze, rain), with the agents learning to optimize their performance in these specific scenarios. The meta-controller is responsible for selecting which agents to use based on their past performance in similar situations. While the decisions are driven by learned policies and experience, the selection process is determined by the needs of the scenario and the agents' historical success."}, {"review_id": "nrT7SB2v3H", "reviewer": "Reviewer_KNba", "summary": "This paper intent to restore the real-world degraded image using a universal model. They first produce a huge dataset containing 1 million images. Further, they apply a pretrain and finetune strategy, which first pretrain on synthetic data and finetune on real-word data based on reinforcement learning. And a multi-agent system is proposed to restore real-world degraded image.", "questions": "1) Could you make the comparison of model efficiency? In my understanding, your proposed Muti-Agent System requires run the single model many times.\n2) It is not clear how to train the Muti-Agent System. And more inference details about this agent will be helpful to the reader.\n3) In Muti-Agent System, does the restoration is processed step by step? If a degraded image contain rain, haze, and snow, does this agent needs to run three or more single model many times? To my knowledge, previous works could remove rain, haze, and snow just run the model once. What are the advantages of your agent?\n4) Overall, I think this is a interesting work, I will reconsider my rate according to your rebuttal for the weaknesses and the questions.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1) The dataset they produced is beneficial to the image restoration fields if the author could make them public available.\n2) The application of Group Relative Policy Optimization to image restoration task is interesting, which the real-world degraded images lacks ground truth to conduct supervision learning.\n3) Introducing the agent to the image restoration is interesting.\n4) The experiment results show that the proposed method outperforms baseline models.\n\nWeaknesses\n1) Perturbing the model parameter to produce multiple results have many previous researches, please add the related reference. And it is better to claim your improvement if you don’t follow their method completely.\n2) I am not sure if you introduce a new reinforcement learning method for image restoration or you just connect ‘perturbing model parameter’ and ‘reinforcement learning’ in series.\n3) The comparison with baseline models is not fair. The dataset you produce is huger than other dataset, as we all know, the model training on huger data will usually lead to better performance. Do you train baseline models in your dataset? If not, the main contribution may lay on your proposed dataset rather than the model.\n4) You state in line 277: ‘To evaluate the effectiveness of our high-fidelity synthetic dataset……’, but in Table 4, the ablation data you used seems not the multi-scene dataset. If the training set only contain singe degraded image, it is impossible to restore mixed degraded image. Anyway, Table 4 can demonstrate the effectiveness to some extend.", "was_revised": true, "final_justification": "The author has addressed my concerns, so I will raise my rate to 5.", "rebuttal": "We sincerely thank the reviewer for the valuable comments and constructive suggestions, which have played an important role in helping us further improve our work. Your review has been extremely helpful in refining our thinking and enhancing the quality of the paper. We also sincerely appreciate your recognition of the novelty and effectiveness of our work. Below, we provide detailed responses to your comments, with related issues grouped together for clarity. All corresponding discussions and additional experimental results will be included in the final camera-ready version.\n\n---\n**[W1] Related works:**\n\nThank the reviewer for the suggestion. We will add citations to prior works that use perturbations to generate diverse outputs, including DFPIR (CVPR 2025) for degradation-aware feature perturbation [DFPIR, CVPR’25], and earlier latent-space or parameter-based methods such as Deep Mean‑Shift Priors and Autoencoding Priors for image restoration [Bigdeli et al., ICCV’17; Aittala & Durand, ICCV’17]. Unlike these, our approach applies RL-guided parameter perturbations with IQA-based reward filtering, and introduces a dual-level structure with a global meta-controller for adaptive model coordination, which distinguishes our method.\n\n---\n**[W2&Q2] Details:**\n\nThank you for the question. Our contribution is the novel adaptation of RL to image restoration via the proposed Perturbation-driven Image Quality Optimization (PIQO). Unlike standard perturbation or prior RL methods, PIQO injects Gaussian noise into model parameters to generate diverse outputs, filters them using no-reference IQA metrics, and applies a GRPO-inspired update based on group-wise advantages—all without paired supervision. This design enables RL-based training in a domain with deterministic outputs and sparse reward signals, making it, to our knowledge, the first framework to successfully adapt GRPO-style optimization to real-world image restoration. The novelty of our method is acknowledged by *Reviewer oVun-S1* and *Reviewer jNge-S2*, such as “The proposed two-level reinforcement-learning strategy (PIQO and meta-agent) is novel and effective for real-world weather restoration.”\n\n---\n**[W3] Comparison:**\n\nFirst, we have reported the performance of the model trained on the proposed dataset without the RL training (PIQO) and agent framework in Table 5 (basic) in paper. The results clearly show the improvement of the proposed PIQO and agent framework. Second, we re-trained WGWS (CVPR,2023) and OneRestore (ECCV,2024) on our dataset, and here are the results. Will add the new results into the paper. \n\n| Methods       | Snow (LIQE) | Snow (CLIP-IQA) | Snow (Q-align) | Snow (MUSIQ) | Haze (LIQE) | Haze (CLIP-IQA) | Haze (Q-align) | Haze (MUSIQ) | Rain (LIQE) | Rain (CLIP-IQA) | Rain (Q-align) | Rain (MUSIQ) |\n|---------------|-------------|-----------------|----------------|--------------|-------------|-----------------|----------------|--------------|-------------|-----------------|----------------|--------------|\n| **WGWS**      | 3.71        | 0.54            | 3.40           | 65.67        | 3.31        | 0.40            | 2.57           | 56.76        | 3.81        | 0.47            | 2.70           | 56.62        |\n| **OneRestore**| 3.73        | 0.55            | 3.56           | 65.32        | 3.38        | 0.41            | 2.67           | 57.13        | 3.83        | 0.48            | 2.68           | 58.67        |\n| **Ours**      | 3.84        | 0.56            | 3.70           | 66.30        | 3.53        | 0.44            | 3.01           | 63.60        | 3.93        | 0.53            | 2.98           | 60.43        |\n\n---\n**[W4] Effectiveness:**\n\nThank you for the observation. While some datasets in Table 4 focus on single degradation types, we also include more diverse datasets such as RealSnow and SPA+, which cover complex and mixed weather conditions. Additionally, our HFLS-Weather dataset supports mixed-weather synthesis (e.g., snow+haze, rain+haze), and the “Our Snow+Haze” and “Our Rain+Haze” entries reflect this capability. These results demonstrate that our dataset not only provides high fidelity but also enables robust performance across diverse and dynamic degradation scenarios. We will revise the text in Line 277 to clarify this point.\n\n---\n**[Q1] Efficiency:**\nThank you for the question. While our Multi-Agent System involves running multiple specialized models, which increases latency (570ms), it offers better restoration quality for complex or mixed weather conditions compared to single-model methods like OneRestore (13ms). Though slower than single-model systems, it is more efficient than other multi-agent methods like JarvisIR (15250ms). We will include a detailed efficiency-performance comparison in the revision to highlight these trade-offs.\n\n| Methods     | Latency  |\n|-------------|----------|\n| **WGWS**    | 68ms     |\n| **Chen et al** | 33ms  |\n| **PromptIR** | 151ms  |\n| **DACLIP**   | 6543ms  |\n| **OneRestore** | 13ms  |\n| **JarvisIR** | 15250ms  |\n| **Ours**     | 570ms   |\n\n---\n**[Q3]:**\nYes, our system restores images step by step by adaptively selecting and sequencing specialized agents based on scene analysis and prior success rates. Unlike one-shot models, it uses IQA feedback to refine execution paths, enabling more accurate restoration under complex, mixed-weather conditions."}, {"review_id": "bEkAq23Joe", "reviewer": "Reviewer_oVun", "summary": "This manuscript proposes a dual-level reinforcement-learning framework for restoring real-world images degraded by rain, haze, and snow. It first builds HFLS-Weather, a million-image, depth-aware synthetic dataset that yields strong weather-specific “cold-start” networks. At the local level, each network is refined on unpaired real photos through Perturbation-Driven Image Quality Optimization, which perturbs model weights and selects improvements using a no-reference perceptual reward. At the global level, a meta-controller treats the specialists as agents and learns, from scene cues and past rewards, how to invoke or chain them for mixed degradations. Extensive experiments confirm that this cold-start plus two-stage RL scheme achieves state-of-the-art quality across multiple real-weather benchmarks.", "questions": "See weakness. I am also curious about the source of the gains brought by the proposed HFLS-Weather dataset. Do they stem mainly from the more accurate depth estimates obtained with Depth-Anything, or from the larger dataset scale?", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. The proposed two-level reinforcement-learning strategy (PIQO and meta-agent) is novel and effective for real-world weather restoration.\n2. The manuscript is well organized and written in clear, straightforward language, making the technical ideas easy to grasp.\n\n**Weaknesses**\n\n1. Reward–metric coupling. The RL reward is built from the same no-reference IQA scores that later serve as evaluation metrics. This tight coupling risks over-fitting the policy to those specific measures and weakens claims of fair comparison. Distinct reward and test metrics would provide a more balanced assessment.\n2. In Figure 4, the Basic + PIQO + Agent result removes weather effects but shows a visible colour shift, while Basic + PIQO keeps the colours closer to the input. Repeated passes through multiple agents seem to chase higher IQA scores at the expense of faithful colours. Have the authors considered how this issue might be mitigated?\n3. The manuscript does not break down how different PIQO settings (e.g., KL threshold, reward mix) affect the outcome. More detailed ablation studies would help show which parts of the method matter most.", "was_revised": true, "final_justification": "I have carefully read the authors’ response as well as the comments from other reviewers. The authors have addressed the main concerns and proposed concrete improvements. I encourage them to include the resolution of the color shift issue and relevant analysis in the revised version. Considering the current contributions and remaining issues, I decide to keep my original score of 5.", "rebuttal": "We sincerely thank the reviewer for the valuable comments and constructive suggestions, which have played an important role in helping us further improve our work. Your review has been extremely helpful in refining our thinking and enhancing the quality of the paper. We also sincerely appreciate your recognition of the novelty and effectiveness of our work. Below, we provide detailed responses to your comments. All corresponding discussions and additional experimental results will be included in the final camera-ready version.\n\n---\n**[W1]:** Thank the reviewer for this insightful comment. In addition to the IQA metrics used for training, we incorporate MUSIQ exclusively for evaluation and include GPT-4o-based perceptual scores and visual comparisons as independent assessments at test time, helping to mitigate potential reward–metric coupling and ensure fair evaluation. We will clarify this issue in revision.\n\n---\n**[W2]:** Thank you for the observation. We acknowledge that repeated agent passes may cause color shifts while optimizing IQA scores. To address this, we plan to explore adding global color consistency terms (e.g., ΔE distance) to the reward and applying a lightweight color correction module post-restoration and also plan to investigate learning a reward function that balances perceptual quality with fidelity to input color distributions.\n\n---\n**[W3]:** Thank you for the suggestion. PIQO settings like the KL threshold and reward weights have a critical impact on convergence. We set $τ = 0.01$ to balance stability and learning speed—lower values may slow or stall convergence, while higher ones can cause instability. If training fails to converge, we recommend reducing $τ$ (e.g., from 0.01 to 0.005 or 0.001) or relaxing the reward filtering criteria to retain more candidate samples. For reward weights, we prioritize CLIP-IQA (w₂ = 1) and assign smaller weights to LIQE and Q-Align (w₁, w₃ = 0.2) to ensure perceptual alignment while maintaining diversity. We will include tuning guidance in the revision.\n\n---\n**[Q]:** Thank you for the question. The gains from HFLS-Weather come from both Depth-Anything’s accurate depth and the large, diverse dataset scale. Depth improves realism in weather simulation (e.g., fog depth cues), while scale enhances generalization. As shown in Table 4, both contribute complementary benefits, and we will clarify this in the revision."}, {"review_id": "4W6w61z3RX", "reviewer": "Reviewer_jNge", "summary": "The paper introduces HFLS-Weather, a high-fidelity synthetic dataset with one million clean/degraded pairs that couples depth-aware haze, rain, and snow simulation to correct shortcomings in prior rain- or haze-only benchmarks.  Using models pre-trained on this dataset as “cold starts,” the authors build a dual-level reinforcement learning framework: (i) local adaptation via Perturbation-Driven Image Quality Optimization (PIQO), which injects Gaussian parameter perturbations and optimizes a composite no-reference reward without paired supervision ; and (ii) a global meta-controller that selects and orders weather-specific agents according to CLIP-based scene analysis and historical success rates.", "questions": "1. Could you clarify how you selected the relative weights assigned to different IQA models in the PIQO reward, and whether training stability changed when those weights were perturbed?\n\n2. The reward signal is a linear combination of no-reference metrics; have you considered a learned reward network trained on small amounts of rated data, and how might that compare with the hand-designed formula?\n\n3. Can you provide more comparison results with the updated methods?\n\nI am very willing to improve my score if the author has well addressed my concerns.", "limitations": "The author points out that their method introduces additional overhead in terms of computation cost, but the analysis of limitations and failure cases is not comprehensive.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "S1. HFLS-Weather supplies depth-consistent multi-weather degradation at a scale far larger than previous datasets, enhancing generalization to real conditions.\n\nS2. PIQO successfully extends GRPO-style reinforcement learning to image restoration, eliminating the need for ground-truth clean images while still providing stable training through KL-regularised updates.\n\nS3. The two-tier design adapts both model parameters and model selection, giving the system flexibility to handle mixed or sequential degradations that defeat single-network baselines.\n\nS4. Across three weather types the method shows consistent, sizeable gains and superior GPT-4o judgements, with detailed ablations clarifying where the improvements originate.\n\nW1. Both training and inference raise resource demands; the authors acknowledge additional latency but do not quantify it.\n\nW2. The composite reward relies on hand-tuned weights for no-reference metrics whose correlation with human perception can vary, yet the impact of these weights is not explored.\n\nW3. The scheduler restricts participation to three agents per image, and there is no analysis of how performance or runtime would change with more degradation types or a larger agent library.\n\nW4. Lack of analysis and verification of extreme degradation scenarios.\n\nW5. The comparative experiment is insufficient. It only compares with some methods in the past, and lacks comparison with the existing latest agent-based methods and the latest All-in-one image restoration methods.", "was_revised": true, "final_justification": "I have read the authors’ response as well as the concerns raised by the other reviewers. The authors’ reply has addressed some of my concerns. I have determined my final score based on this.", "rebuttal": "We sincerely thank the reviewer for the valuable comments and constructive suggestions, which have played an important role in helping us further improve our work. Your review has been extremely helpful in refining our thinking and enhancing the quality of the paper. We also sincerely appreciate your recognition of the novelty and effectiveness of our work. Below, we provide detailed responses to your comments, with related issues grouped together for clarity. All corresponding discussions and additional experimental results will be included in the final camera-ready version.\n\n---\n**[W1] Resource Overhead:**\n\nThank the reviewer for the question. We report that cold-start training of the lightweight single-agent on eight parallel RTX 4090 GPUs required ~72 hours with a 23 GB peak memory footprint. The complete PIQO + Agent framework—trained thereafter on a single RTX 4090—took ~48 hours with a 22 GB peak. During inference, the full framework averages 0.57 s/image (17 GB peak), whereas the lightweight PIQO variant averages 0.027 s/image (4.3 GB peak). It is worth noting that our method demonstrates advantages over agent-based JarvisIR (CVPR 2025) during inference; see [Reviewer YKa4, Limitation 1]. We will include these figures and discuss deployment trade-offs in the revision.\n\n---\n**[W2&Q2] Reward Design:**\n\nThank you for the question. We observed that using a single IQA model as the reward could lead to overfitting—while the metric score improved, human-perceived quality often deteriorated. The intuition behind this is that the model can optimize too closely to the reward metric, without capturing the broader perceptual nuances. To mitigate this, we adopted a composite reward combining multiple IQA models with complementary strengths, which helps balance different aspects of perceptual quality and aligns well with human observation. Each score is normalized to [0, 1] to ensure fair weighting across models. Although the weights are hand-tuned, this approach stabilizes training and prevents the model from overfitting to a single metric. Additionally, if training does not converge or exhibits early signs of overfitting (e.g., performance regression despite rising metric scores), we stop early or adjust the weights to restore a better balance. We will include more details on the tuning process and stopping criteria in the revision to clarify how we ensure robust performance.\n\n---\n**[W3] Expansibility:**\n\nThank you for your question. To assess the impact of agent library size on performance, we expanded the weather-related agent pool from three to four by adding a Raindrop degradation agent. In real rainy scenarios, this resulted in significant improvements across multiple IQA metrics, suggesting that additional agents can enhance restoration quality. However, the larger agent pool also introduces increased scheduling complexity and inference latency. Future work will explore adaptive agent selection and dynamic scheduling to optimize the trade-off between performance gains and computational cost.\n| Methods          | LIQE  | CLIP-IQA | Q-Align | MUSIQ | Latency |\n|------------------|-------|----------|---------|-------|---------|\n| **Basic**        | 4.02  | 0.56     | 3.29    | 64.11 | 567ms   |\n| **Basic + RainDrop** | 4.14  | 0.59     | 3.37    | 67.42 | 595ms   |\n\n---\n**[W4] Extreme Degradation Scenarios:** \n\nThank you for the comment. We agree that extreme degradation scenarios are important to evaluate. In fact, we have included corresponding analysis and visual results in the supplementary material (Sections B & C), where we test our model on severe fog, heavy snow, and dense rain conditions. The results show its effectiveness under extreme weather. We will clarify this in the main paper and explicitly reference the supplementary section.\n\n---\n**[W5&Q3]  Further Comparisons:**\n\nWe perform the experiments with JarvisIR  and DFPIR (CVPR 2025) on WReaL data and show the results below. It is worth noting that our method significantly outperforms JarvisIR in both inference speed and resource efficiency. Our method takes on average 570 ms and 17 GB peak memory per image, while JarvisIR requires 15,250 ms and 29.5 GB peak memory. The performance is evaluated by GPT-4o; see paper for details.\nNotably, our method demonstrates superior performance compared to these strong competitors.\n\n| Weather | Metric             | DFPIR | JarvisIR | Ours  |\n|---------|--------------------|-------|----------|-------|\n| **Snow** | Artifact Removal    | -  | 3.57     | 4.42  |\n|         | Weather Resilience  | -  | 3.61    | 4.35  |\n|         | Overall Visual Quality | - | 3.73     | 4.39  |\n| **Haze** | Artifact Removal    | 3.17  | 3.65     | 4.07  |\n|         | Weather Resilience  | 3.14  | 3.45     | 4.02  |\n|         | Overall Visual Quality | 3.24 | 3.58     | 3.95  |\n| **Rain** | Artifact Removal    | 3.34  | 3.71     | 4.25  |\n|         | Weather Resilience  | 3.15  | 3.69     | 4.07  |\n|         | Overall Visual Quality | 3.18 | 3.87     | 3.90  |\n\n---\n**Q1:**\nThank you for the question. The weights assigned to different IQA models in the PIQO reward were chosen based on their relevance to perceptual quality, with CLIP-IQA receiving the highest weight (w₂ = 1) due to its strong alignment with human visual quality judgments, and LIQE and Q-Align given smaller weights (w₁, w₃ = 0.2) to ensure diversity. Perturbing these weights during training showed that increasing CLIP-IQA’s weight sped up convergence but sometimes overfitted on perceptual details, while lowering weights for LIQE and Q-Align slowed convergence but maintained better balance. Large weight changes led to training instability.\n\n---\n**Limitations:**\nThank you for the feedback. We acknowledge that while our method introduces additional computational overhead, the analysis of limitations and failure cases could be more comprehensive. The increased computational cost primarily stems from the multi-agent system and the iterative optimization process. Nevertheless, our method demonstrates advantages over JarvisIR. In terms of failure cases, the system may struggle when the agent pool becomes too large, leading to increased inference latency and scheduling complexity. Additionally, when faced with highly complex or unseen degradation types, the system may require more agents, which can affect runtime efficiency. To mitigate these issues, we are exploring adaptive agent selection and dynamic scheduling to optimize performance while managing computational cost."}]

Author rebuttal:

Meta-review: This paper proposes a dual-level reinforcement learning framework for real-world adverse weather image restoration, combining a high-fidelity synthetic dataset (HFLS-Weather), a perturbation-driven PIQO module, and a meta-controller for adaptive agent scheduling. The approach is well-motivated and shows strong performance across rain, haze, and snow, with clear benefits over prior single-model and multi-agent baselines. Strengths include the novel adaptation of GRPO-style RL to unpaired restoration, the scale and fidelity of the synthetic dataset, and extensive ablations clarifying contributions. Weaknesses raised by reviewers concerned fairness of comparisons, efficiency, reward–metric coupling, and color fidelity. The rebuttal addressed these points with retrained baselines, runtime/memory reports, added evaluation metrics, and solutions for color shift, which satisfied most reviewers and improved scores. While algorithmic novelty is incremental, the integration of components is coherent, impactful, and practically relevant. Overall, I recommend acceptance given the strong results, thorough evaluation, and community value of both the framework and dataset.
Decision: Accept (poster)