Adaptive Dynamic Dehazing via Instruction-Driven and Task-Feedback Closed-Loop Optimization for Diverse Downstream Task Adaptation

Yafei Zhang; Shuaitian Song; Huafeng Li; Shujuan Wang; Yu Liu

cs.CVFeb 28, 2026

Adaptive Dynamic Dehazing via Instruction-Driven and Task-Feedback Closed-Loop Optimization for Diverse Downstream Task Adaptation

Authors: Yafei Zhang, Shuaitian Song, Huafeng Li, Shujuan Wang, Yu Liu

Organizations: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China · School of Instrument Seicence and Opto-electronics Engineering, Hefei University of Technology, Hefei 230009 , China

Abstract

In real-world vision systems,haze removal is required not only to enhance image visibility but also to meet the specific needs of diverse downstream tasks.To address this challenge,we propose a novel adaptive dynamic dehazing framework that incorporates a closed-loop optimization mechanism.It enables feedback-driven refinement based on downstream task performance and user instruction-guided adjustment during inference,allowing the model to satisfy the specific requirements of multiple downstream tasks without retraining.Technically,our framework integrates two complementary and innovative mechanisms: (1)a task feedback loop that dynamically modulates dehazing outputs based on performance across multiple downstream tasks,and (2) a text instruction interface that allows users to specify high-level task preferences.This dual-guidance strategy enables the model to adapt its dehazing behavior after training,tailoring outputs in real time to the evolving needs of multiple tasks.Extensive experiments across various vision tasks demonstrate the strong effectiveness,robustness,and generalizability of our approach.These results establish a new paradigm for interactive,task-adaptive dehazing that actively collaborates with downstream applications.

Figures & tables

Figure 1: Comparison between existing methods and ours.

Figure 2: Overview of the proposed method. The method constructs a closed-loop regulation mechanism jointly guided by semantic task instructions and task performance feedback. It leverages the semantic features of text instructions extracted by BERT, the initial dehazed images, and feedback from downstream tasks to collaboratively adjust dehazing features via the IGM and TFGA modules, enabling adaptive optimization across diverse downstream scenario.

Figure 5: Visual comparison with state-of-the-art methods on Setting 1. Each part includes two rows: dehazing results (rows 1) and corresponding downstream task outputs (rows 2). Input images are taken from ADE20K, COCO, and KITTI.

Methods	ADE20K			COCO			KITTI
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Dehamer ( Guo et al. 2022 )	22.63	0.8964	0.1538	23.26	0.9280	0.1369	24.87	0.9212	0.1029
C2P ( Zheng et al. 2023 )	22.26	0.8842	0.1594	23.02	0.9044	0.1322	25.01	0.9342	0.0926
RIDCP ( Wu et al. 2023 )	27.21	0.9207	0.0702	28.18	0.8990	0.0974	27.18	0.9441	0.1242
Dehazeformer ( Song et al. 2023 )	23.76	0.8958	0.1332	22.41	0.8639	0.1412	23.36	0.9160	0.1163
KA-Net ( Feng et al. 2024 )	24.57	0.9153	0.0914	24.92	0.9241	0.1291	26.73	0.9413	0.0968

Table 1: Comparison of dehazing performance. Best and runner-up values are highlighted in bold and underlined, respectively.

Methods	SS	OD		DE (Error Metric $\downarrow$ )				DE (Accuracy Metric $\uparrow$ )
Methods	mIoU $\uparrow$	mAP $\uparrow$	mAP50-95 $\uparrow$	AbsRel	SqRel	RMSE	RMSElog	$\delta<1.25$	$\delta<1.25^{2}$	$\delta<1.25^{3}$
Dehamer	44.04	49.8	30.4	0.115	0.860	4.802	0.195	0.874	0.956	0.980
C2P	46.66	52.3	33.5	0.123	0.888	4.933	0.206	0.855	0.943	0.972
RIDCP	46.15	51.7	33.0	0.100	0.680	4.307	0.175	0.898	0.966	0.984
Dehazeformer	45.84	52.0	33.2	0.105	0.711	4.431	0.182	0.889	0.964	0.983
KA-Net	45.47	50.5	31.2	0.108	0.749	4.554	0.187	0.884	0.960	0.982

Table 2: Comparison of downstream task performance across semantic segmentation (SS), object detection (OD), and depth estimation (DE) on Setting 1. Best and runner-up values are highlighted in bold and underlined, respectively.

Figure 6: Visual comparison of dehazing results from ablation studies.

Methods	ADE20K			COCO			KITTI
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Baseline	23.47	0.9255	0.0791	22.83	0.9058	0.1151	25.37	0.9091	0.0701
Baseline + FFM	25.27	0.9522	0.0460	24.73	0.9368	0.0860	27.08	0.9333	0.0353
Baseline + FFM + IGM	26.36	0.9646	0.0367	25.89	0.9515	0.0749	29.00	0.9664	0.0241
Baseline + FFM + TFGA	26.62	0.9672	0.0333	26.42	0.9550	0.0741	29.30	0.9688	0.0223
Full Model (All Modules)	27.47	0.9701	0.0293	27.14	0.9587	0.0698	30.50	0.9740	0.0176

Table 3: Ablation studies on the dehazing performance.

Methods	SS	OD		DE (Error Metric $\downarrow$ )				DE (Accuracy Metric $\uparrow$ )
Methods	mIoU $\uparrow$	mAP $\uparrow$	mAP50-95 $\uparrow$	AbsRel	SqRel	RMSE	RMSElog	$\delta{<}1.25$	$\delta{<}1.25^{2}$	$\delta{<}1.25^{3}$
Baseline	49.42	53.5	34.8	0.113	0.753	4.539	0.172	0.874	0.941	0.982
Baseline + FFM	50.25	54.6	35.6	0.100	0.666	4.321	0.175	0.897	0.966	0.984
Baseline + FFM + IGM	50.28	54.6	35.5	0.100	0.664	4.318	0.175	0.898	0.966	0.984
Baseline + FFM + TFGA	50.29	54.6	35.7	0.100	0.663	4.316	0.175	0.898	0.967	0.984
Full Model (All Modules)	50.34	54.7	35.7	0.099	0.662	4.310	0.174	0.898	0.967	0.984

Table 4: Ablation experiments on downstream tasks.

Figure 7: Closed-loop optimization framework in the inference phase.

Figure 8: Model complexity analysis. The x-axis in the figure represents the FLOPs (in billions) obtained by the model on a 256×256 image input, the y-axis represents PSNR(dB), and the bubble size indicates the number of parameters.

Figure 9: Visual comparison with state-of-the-art methods on Setting 1. The results are organized into three parts according to downstream tasks, each comprising four rows: dehazing results (rows 1) and corresponding downstream task outputs (rows 2). Input images are taken from ADE20K, COCO, and KITTI.

Figure 10: Visual comparison with state-of-the-art methods on Setting 2. The results are organized into three parts according to downstream tasks, each comprising four rows: dehazing results (rows 1 and 3) and corresponding downstream task outputs (rows 2 and 4). Input images are taken from ADE20K, COCO, and KITTI.

Figure 11: Visual comparison with state-of-the-art methods on Setting 3. The results are organized into three parts according to downstream tasks, each comprising four rows: dehazing results (rows 1 and 3) and corresponding downstream task outputs (rows 2 and 4). Input images are taken from ADE20K, COCO, and KITTI.

Methods	SS	OD		DE (Error Metric ↓)				DE (Accuracy Metric ↑)
Methods	mIoU↑	mAP↑	mAP50-95↑	AbsRel	SqRel	RMSE	RMSElog	$\delta{<}1.25$	$\delta{<}1.25^{2}$	$\delta{<}1.25^{3}$
Dehamer ( Guo et al. 2022 )	43.46	43.8	25.6	0.123	0.894	5.102	0.202	0.853	0.953	0.980
C2P ( Zheng et al. 2023 )	44.10	45.2	29.2	0.152	1.143	5.782	0.235	0.792	0.925	0.966
RIDCP ( Wu et al. 2023 )	43.73	47.5	30.4	0.104	0.716	4.414	0.179	0.890	0.966	0.984
Dehazeformer ( Song et al. 2023 )	44.69	46.8	30.0	0.125	0.850	5.027	0.202	0.849	0.963	0.980
KA-Net ( Feng et al. 2024 )	43.67	44.3	26.6	0.117	0.826	4.776	0.193	0.867	0.957	0.982

Table 5: Comparison of the proposed method across semantic segmentation (SS), object detection (OD), and depth estimation (DE) on Setting 2. Best and runner-up values are highlighted in bold and underlined, respectively.

Methods	SS	OD		DE (Error Metric ↓)				DE (Accuracy Metric ↑)
Methods	mIoU↑	mAP↑	mAP50-95↑	AbsRel	SqRel	RMSE	RMSElog	$\delta{<}1.25$	$\delta{<}1.25^{2}$	$\delta{<}1.25^{3}$
Dehamer ( Guo et al. 2022 )	44.32	49.5	31.8	0.109	0.852	4.762	0.192	0.882	0.958	0.981
C2P ( Zheng et al. 2023 )	47.14	52.2	33.4	0.119	0.884	4.892	0.196	0.862	0.951	0.980
RIDCP ( Wu et al. 2023 )	49.18	51.6	33.1	0.101	0.690	4.320	0.175	0.897	0.966	0.985
Dehazeformer ( Song et al. 2023 )	47.41	50.1	32.2	0.110	0.756	4.554	0.185	0.878	0.962	0.983
KA-Net ( Feng et al. 2024 )	45.28	51.2	31.9	0.107	0.750	4.467	0.189	0.881	0.958	0.982

Table 6: Comparison of the proposed method across semantic segmentation (SS), object detection (OD), and depth estimation (DE) on Setting 3. Best and runner-up values are highlighted in bold and underlined, respectively.

$\lambda$	SS	OD		DE (Error Metric ↓)				DE (Accuracy Metric ↑)
$\lambda$	mIoU↑	mAP↑	mAP50-95↑	AbsRel	SqRel	RMSE	RMSElog	$\delta{<}1.25$	$\delta{<}1.25^{2}$	$\delta{<}1.25^{3}$
0	50.21	53.4	34.8	0.101	0.691	4.44	0.184	0.879	0.956	0.976
0.01	50.29	53.9	35.1	0.101	0.683	4.41	0.179	0.886	0.963	0.981
0.1	50.34	54.7	35.7	0.099	0.662	4.31	0.174	0.898	0.967	0.984
1	50.33	54.4	35.5	0.099	0.683	4.33	0.175	0.897	0.967	0.984

Table 7: Comparison of the proposed method across semantic segmentation (SS), object detection (OD), and depth estimation (DE). Bold indicates the best result.

$\gamma$	SS	OD		DE (Error Metric ↓)				DE (Accuracy Metric ↑)
$\gamma$	mIoU↑	mAP↑	mAP50-95↑	AbsRel	SqRel	RMSE	RMSElog	$\delta{<}1.25$	$\delta{<}1.25^{2}$	$\delta{<}1.25^{3}$
0	50.18	53.5	34.9	0.102	0.691	4.43	0.183	0.878	0.955	0.976
0.01	50.34	54.7	35.7	0.099	0.662	4.31	0.174	0.898	0.967	0.984
0.1	50.31	54.5	35.6	0.101	0.664	4.33	0.177	0.896	0.964	0.984
1	50.32	54.7	35.6	0.099	0.663	4.32	0.174	0.897	0.965	0.984

Table 8: Comparison of the proposed method across semantic segmentation (SS), object detection (OD), and depth estimation (DE). Bold indicates the best result.

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Bilevel Layer-Positioning LoRA for Real Image Dehazing

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School of Computer Science and Engineering, Sun Yat-sen University · Dalian University of Technology · Xidian University+1

Image Dehazing Low-Rank Adaptation

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Learning-based real image dehazing methods have achieved notable progress, yet they still face adaptation challenges in diverse real haze scenes. These challenges mainly stem from the lack of effective unsupervised mechanisms for unlabeled data and the heavy cost of full model fine-tuning. To address these challenges, we propose the haze-to-clear text-directed loss that leverages CLIP's cross-modal capabilities to reformulate real image dehazing as a semantic alignment problem in latent space, thereby providing explicit unsupervised cross-modal guidance in the absence of reference images. Furthermore, we introduce the Bilevel Layer-positioning LoRA (BiLaLoRA) strategy, which learns both the LoRA parameters and automatically search the injection layers, enabling targeted adaptation of critical network layers. Extensive experiments demonstrate our superiority against state-of-the-art methods on multiple real-world dehazing benchmarks. The code is publicly available at https://github.com/YanZhang-zy/BiLaLoRA.

Abstract

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