Title: Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image restoration

adverse weather

deraining

dehazing

desnowing

and reinforcement learning
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

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

1 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-based methods are developed from adverse weather image restoration, such as weatherspecific models for deraining [15, 61, 20, 68, 21, 54, 14], dehazing [19, 4, 13, 44, 48], desnowing [32], and all-in-one models for multiple weather types [27, 46, 11, 35, 69, 49, 63, 36, 58, 8].

Despite these advances, existing methods often struggle in real-world applications due to a fundamental limitation: models trained on synthetic data fail to generalize effectively to unpredictable real-world degradations. This performance gap arises from three limitations in current approaches: (i) existing multi-weather 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) singlemodel architectures cannot leverage dynamic coordination strategies to optimally handle diverse and multiple degradation types.

To address these, we develop a self-evolving approach that contains the physics-driven synthetic data generation with a dual-level reinforcement learning architecture. First, we create the High-Fidelity Large-Scale Weather dataset (HFLS-Weather), which simulates weather artifacts like rain, fog, and

snow based on their physical formation. This dataset contains one million images, using depth information predicted by a 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-level 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-Weather dataset and then continuously refined based on real-world feedback through reinforcement learning. At the global level, a meta-controller dynamically coordinates the collaboration of individual restoration models (agents) by analyzing degradation patterns and historical execution data. This dual-level synergy establishes a closedloop learning ecosystem: at the local level, individual restoration models continuously refine their capabilities 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-level system on real data without paired ground-truth images. Reinforcement learning offers a potential solution, as it doesn't require pixellevel supervision, making it suitable for scenarios where real data, particularly in adverse weather, lacks paired ground-truth images. However, unlike recent successful reinforcement learning applications 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 [43], 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 Optimization ( PIQO ), which modifies GRPO [42] in two aspects to make it suitable for image restoration tasks. First, we introduce perturbations to network parameters, enabling the model to generate different results for a 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 weather-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 to successfully apply GRPO concepts to image restoration, demonstrating that a 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, 4, 13, 44, 48], and snow [32]. More recent efforts aim to develop unified frameworks for general adverse weather removal [27, 46, 11, 35, 69, 49, 63, 36]. All-in-One [27] first unified weather restoration via joint training; TransWeather [46] introduced transformer-based adaptive queries; and Chen et al. [11] used contrastive learning with knowledge distillation. WeatherDiff [35] proposed a diffusion-based model, while WGWS [69] employed a twostage pipeline for general-to-specific refinement. WeatherStream [64] introduced real degraded-clean pairs but suffered from compression noise. Although these approaches have shown impressive results on synthetic benchmarks [15, 38, 32, 25, 26, 20, 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.

(a) Ours

(c)RESIDE-OTS

(d)RainCityscapes

(e) Snow100K

2.2 Large-Scale, Agent-Based, and Perturbation Methods

Recent advancements leveraged large-scale models and multi-agent systems to address image restoration challenges across various adverse weather conditions. DA-CLIP [33] extends CLIP [39] via a dynamic controller for robust embeddings. Other methods integrate external knowledge: e.g. , distilling semantics 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-based restoration, including synthetic degradation generation. Very recently, JarvisIR [29] also adopts multi-agent RL strategies for weather-degraded image restoration.

Some works explore perturbation-based mechanisms for improving restoration diversity and robustness. DFPIR [45] introduces degradation-aware feature perturbation, while earlier works exploit latent-space or parameter perturbations through learned priors, such as deep mean-shift priors [3] and autoencoding priors [2]. Unlike these, our method employs RL-guided parameter perturbations with IQA-based reward filtering, and further introduces a dual-level structure where a global metacontroller dynamically coordinates local restoration agents, enhancing adaptability in real-world scenarios. Although GPT-4o 2 shows potential in visual editing ( e.g. , removing weather artifacts), it often produces visually appealing but physically unauthentic outputs ( e.g. , hallucinated objects, distorted structure) [9], limiting its utility in tasks requiring geometric and photometric 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 on synthetic images generated via atmospheric scattering models [16], with depth maps sourced from LiDAR [41, 20] or monocular depth estimation [30]. While widely used for weather artifact removal, these datasets suffer from key limitations: (i) LiDAR-based collection is expensive and limited in scale and scene diversity; (ii) depth maps often lack granularity, introducing unrealistic artifacts such as ghosting (see Fig. 1); and (iii) depth estimation models generalize poorly, further degrading realism.

To overcome these issues, we develop HFLS-Weather , a 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. 1a).

HFLS-Weather offers two core advantages: (i) High-fidelity depth at scale. We generate accurate depth maps from clear-weather images using a state-of-the-art model, eliminating the cost and limitations of LiDAR while enabling realistic synthesis across diverse scenes. (ii) Depth-consistent multi-weather simulation. A unified framework applies depth-driven attenuation not only to haze,

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 [5, 16], incorporating depth-dependent interactions between weather phenomena. In real-world scenarios, distant objects are often obscured by fog, while proximate regions exhibit rain streaks or snowflakes [20, 21]. To model this, we define the weather-affected image I weather ( x ) as

where J ( x ) represents the clean image, M ( x ) ∈ [0 , 1] denotes the rain/snow layer, and F ( x ) = e -βd ( x ) corresponds to the fog layer, with β as the atmospheric scattering coefficient and d ( x ) representing high-quality depth information. A ( x ) is the global atmospheric light. Fog is simulated using a transmission map F ( x ) = e -βd ( x ) , where β 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 M ( x ) , a semi-transparent mask created through procedural generation. Rain streaks appear more intense on closer objects, while snowflakes accumulate in a scattered pattern, adding realism through variable opacity based on 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 Snow100K [32], RESIDE-OTS [25], Google Landmark V2 [51], and OSV5M [1]. 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 1, HFLS-Weather 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-condition learning for advanced weather artifact removal.

4 Dual-Level Reinforcement Learning Framework

In this work, we present a dual-level reinforcement learning framework for real-world adverse weather image restoration, integrating both Perturbation-Driven Image Quality Optimization (PIQO) and a Multi-Agent System to continuously refine restoration models through real-world feedback.

Perturbation-drivenImageQualityOptimization

Trained

Frozen

ParameterUpdate

Models

Models

GeneratingPerturbationModels

RestoredImages

Rewards

Policy

Model

Policy

Ip

rp

Advantages

Inputlmage

Model

Perturbed

Model,

r1

A1

Inject Gaussian

Perturbations

Perturbed

Model2

12

Reward

r2

Reward

f2

Group

A2

··

..

Models

...

Filters

...

Computation

...

Perturbed

Model

Perturbed

Modeln

in

rn

fm

Am

Muti-Agent System

PerceptualAnalysisandBidding

RestorationExecution

GeneratingAWeatherDescription

Rain

Exit

Snow+Haze

Successful Restoration

InputImage

Rain + Haze

Exit

Meta-Controller

Haze

TOP-3 history success rate

AssessQualityImprovement

InputImage

Snow

Snow+Haze

TOP-2history successrate

IQA Models

Improved

IQAModels

Snow

TOP-1history successrate

IQAModels

Not Improved

IQAModels

Inputlmage

TaskFlow

Restored lmage

Meta-Controller

Meta-Controller

PerceptualAnalysisandBidding+RestorationExecution

Snow

Snow

RestoredImage

All Degradation Resolved.

Meta-Controller

PerceptualAnalysisandBidding+RestorationExecution

Congratulations!!!

Clear

Figure 2 illustrates 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 a 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-driven Image Quality Optimization

While reinforcement learning has advanced LLM alignment through techniques like Group Relative Policy Optimization (GRPO) [42], its application to image restoration is less explored. Unlike text generation, which naturally supports one-to-many outputs, image restoration typically follows a 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 [43], often underperform in such underconstrained scenarios.

To address this, we present Perturbation-driven Image Quality Optimization (PIQO), a GRPO-inspired framework tailored for image restoration. PIQO injects small Gaussian perturbations into model parameters during inference to produce diverse outputs for the same input. Let θ be the current model parameters and the i -th perturbed version θ ′ i = θ +∆ . For each degraded input image, we generate multiple outputs ˆ I i using perturbed models f ( θ ′ i ) .

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:

where w ∗ are the weights for each metric. This reward function produces a scalar score r i 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 or even harmful gradients. To mitigate this, we apply a reward filtering step that discards outputs whose rewards fall below that of the unperturbed model, ensuring the optimization focuses only on advantageous directions. Let I p denote the output from the current

model and MUSIQ [23] be the filtering criterion; we retain only the indices with:

Next, we compute the normalized advantage A i for each retained sample i ∈ S , which reflects how much its reward deviates from the group mean:

where ¯ r and σ r denote the mean and standard deviation of the rewards { r j } N j =1 within the retained group S . This relative advantage indicates the quality of a perturbed output compared to the group average: positive A i suggests a beneficial perturbation, while negative A i implies a less favorable one (note that low-quality samples have mostly been filtered out by the r p baseline). Normalizing the advantages reduces variance in the gradient estimate and serves as a 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 g over the filtered subset S :

where the negative sign reflects gradient ascent on reward.

To stabilize updates, we apply implicit KL regularization by approximating divergence in parameter space. This is analogous to the trust-region constraint in PPO, which limits how much the policy can change in a single step.

where | θ | is the number of parameters and θ ′ i,j the j -th parameter of the i -th perturbed model.

We compute a scaling factor based on a KL threshold τ :

The final parameter update is:

where η 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-world data through perturbation-induced diversity, reward-based filtering, and variance-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 a meta-controller (CLIP [39]), which identifies the dominant weather-related 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 of 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.

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 to 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. (1).

If the IQA score decreases compared to the previous round, the restoration is considered a failure. In this case, the system reverts the image to 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 a successful restoration is achieved or 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 is still detected, the restoration is considered partially successful, and the system enters the next round. The bidding process is reinitiated, 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 a 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 to train a DSANet [12]-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 HFLS-Weather dataset, using the Adam optimizer with a batch size of eight and a 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[69]to further improve rain removal. At the local level, task-specific restoration agents ( e.g. , deraining, dehazing, desnowing) are enhanced via Perturbation-driven Image Quality Optimization (PIQO), guided by weighted image quality assessment (IQA) rewards with weights w 1 = 0 . 2 , w 2 = 1 , and w 3 = 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 a learning rate of 0.0001 and a batch size of 16. At the global level, the multi-agent system is further optimized using a batch size of 16 and a learning rate of 0.0001.

Input

WGWS

PromptIR

DA-CLIP

OneRestore

Ours

000

5 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 Snow100K [32].

5.1 Performance Comparison with State-of-the-Art 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 CLIPIQA [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 of 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.[11], WGWS[69], PromptIR[37], OneRestore[18], DA-CLIP[33], DFPIR [45], and JarvisIR [29]. From the results, we observe the following: (i) Our method outperforms all competitors under snow, haze, and rain , demonstrating strong generalization across diverse real-world degradations. (ii) Our method achieves the highest scores in all IQA metrics , reflecting superior visual fidelity and semantic relevance. (iii) GPT-4o's perceptual evaluation highlights our method's excellence in artifact removal,

Input

basic

.

basic+Agent

.

basic+PIQO

.

basic+PIQO+Agent

.

weather resilience, and overall visual quality. (iv) While competing methods fluctuate across different weather conditions, our 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 on 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 or 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 PromptIR (208 ms), our framework is slower but more robust. At the same time, it remains far more efficient than other multi-agent systems like DA-CLIP (6543 ms) and JarvisIR (15250 ms), striking a 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 on both single-degradation and mixed-weather datasets, comparing models initialized on different synthetic datasets and then refined using our PIQO.

As the results in Table 4, we have the following observations. (i) Superiority of High-Quality Cold Start : Models initialized with HFLS-Weather show significant improvements in image quality over those trained on 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 to multiple degradation types improves generalization during PIQO finetuning. Additional comparisons of HFLS-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-Weather synthetic dataset, while Agent denotes the proposed multi-agent coordination system. As shown in Table 5 and Figure 4: (i) adding the PIQO training significantly enhances perceptual 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.

6 Conclusion

We develop a dual-level reinforcement learning framework for real-world adverse weather image restoration, combining a physics-driven synthetic dataset (HFLS-Weather) with a two-tier adaptive learning system. At the local level, weather-specific models are refined using perturbation-driven optimization without paired supervision. At the global level, a meta-controller dynamically schedules model execution based on 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-critical 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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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+Haze' model excels across all metrics, particularly in Q-Align (3.8148) and MUSIQ (56.1142), outperforming models trained on Rain13k 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 HFLS-Weather in generating realistic and high-fidelity weather conditions for image restoration tasks.

A.2 Re-training Other Methods on the HFLS-Weather

A potential concern is that performance improvements may primarily come from the scale of the HFLS-Weather 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 HFLS-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-Weather using their released code. The quantitative results are presented in Table 7. 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-Weather is 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.

B More Comparisons with the State-of-the-Art Methods

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

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(g) Ours

(a) Input

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

(g) Ours

(a) Input

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

(a) Input

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

合营

会善

会善

(a) Input

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

L

TAXI

901B

(a) Input

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(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

49

(g) Ours

(g) Ours

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(g) Ours

(g) Ours

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(e) DACLIP

(f) OneRestore

(g) Ours

复十5折出发

我的城

复升5折出家

告白

我的城

演滴出行

我的城

低十5折出发

我的城

复十5折出发

我的城

复十5折出发

我的城

复5折出发

我的城

(a) Input

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

(a) Input

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

(g) Ours

(a) Input

(c) WGWS

(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

(g) Ours

(a) Input

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(d) PromptIR

(e) DACLIP

(f) OneRestore

(g) Ours

C Additional Results Produced by Our Method

We provide more qualitative results under snow, haze, and rain conditions.

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2

2

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RGXROTA

豫K·J9305

豫K·J9305

因务石家生站场团

冀A·88H99

冀A·88H99

70

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IRE

FIRI

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nS1. 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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nSee 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1) 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nHow 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}