Advancing Visual Reliability: Color-Accurate Underwater Image Enhancement for Real-Time Underwater Missions

Yiqiang Zhou; Yifan Chen; Zhe Sun; Jijun Lu; Ye Zheng; Xuelong Li

cs.CVMar 17, 2026

Advancing Visual Reliability: Color-Accurate Underwater Image Enhancement for Real-Time Underwater Missions

Authors: Yiqiang Zhou, Yifan Chen, Zhe Sun, Jijun Lu, Ye Zheng, Xuelong Li

Organizations: Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China · College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China · College of Future Information Technology, Fudan University, Shanghai 200433, China · School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, China · Fujian Ocean Innovation Center, Xiamen 361102, China

Abstract

Underwater image enhancement plays a crucial role in providing reliable visual information for underwater platforms, since strong absorption and scattering in water-related environments generally lead to image quality degradation. Existing high-performance methods often rely on complex architectures, which hinder deployment on underwater devices. Lightweight methods often sacrifice quality for speed and struggle to handle severely degraded underwater images. To address this limitation, we present a real-time underwater image enhancement framework with accurate color restoration. First, an Adaptive Weighted Channel Compensation module is introduced to achieve dynamic color recovery of the red and blue channels using the green channel as a reference anchor. Second, we design a Multi-branch Re-parameterized Dilated Convolution that employs multi-branch fusion during training and structural re-parameterization during inference, enabling large receptive field representation with low computational overhead. Finally, a Statistical Global Color Adjustment module is employed to optimize overall color performance based on statistical priors. Extensive experiments on eight datasets demonstrate that the proposed method achieves state-of-the-art performance across seven evaluation metrics. The model contains only 3,880 inference parameters and achieves an inference speed of 409 FPS. Our method improves the UCIQE score by 29.7% under diverse environmental conditions, and the deployment on ROV platforms and performance gains in downstream tasks further validate its superiority for real-time underwater missions.

Figures & tables

Fig. 1: Comparison of different methods in the FLOPs–PSNR space on UIEB dataset. The x-axis denotes FLOPs (G, 256 × 256 inputs), y-axis indicates PSNR (dB), and circle size is used to indicate parameter count.

Fig. 2: Overview of our proposed UIE framework. The pipeline begins with the AWCC module, which adaptively compensates degraded color channels and stabilizes luminance through secondary correction. The enhanced image is then processed by a CNN backbone incorporating MRDConv to capture multi-directional features with efficient inference. Finally, the SGCA module leverages global statistical priors to refine color temperature, tint, and saturation, producing high-fidelity enhancement with low computational overhead.

Fig. 3: Visualization of intermediate values for AWCC and the SGCA modules: The red and blue channels are visualized using the AUTUMN and WINTER colormaps, respectively.

Fig. 4: Architecture of the MRDConv and the Re-parameterization process.

Method	Venue	UIEB				LSUI				EUVP-S
Method	Venue	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	UIQM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	UIQM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	UIQM $\uparrow$
U-Shape [ 32 ]	IEEETIP’23	20.518	0.782	0.215	3.280	24.492	0.861	0.148	3.119	23.503	0.827	0.187	3.130
FiveA+ [ 47 ]	BMVC’23	21.566	0.892	0.134	3.092	23.883	0.901	0.164	2.993	25.968	0.887	0.159	2.932
Boths [ 50 ]	IEEEGRSL’23	22.230	0.901	0.156	3.273	21.221	0.851	0.222	3.083	23.487	0.838	0.268	3.038
SFGNet [ 48 ]	ICASSP’24	22.100	0.882	0.140	3.010	24.641	0.899	0.162	2.967	25.815	0.866	0.178	3.037
Lite [ 49 ]	ESWA’24	22.227	0.894	0.130	3.129	20.596	0.825	0.237	2.937	25.139	0.842	0.190	2.946

TABLE I: Quantitative comparison of performance and computational efficiency on UIEB, LSUI, EUVP-S, EUVP-D and EUVP-I datasets. Best results are in bold .

Fig. 5: Comparison of visual performance across different methods on five full-reference benchmark datasets.

Method	UIEB		LSUI
Method	U45	RUIE	U45	RUIE
WaterNet [ 29 ]	0.5480	0.5154	0.5315	0.5116
U-shape [ 32 ]	0.5964	0.5529	0.5654	0.5489
UIE-DM [ 16 ]	0.5854	0.5430	0.5827	0.5700
SFGNet [ 48 ]	0.5937	0.5546	0.5799	0.5564
Lite [ 49 ]	0.5748	0.5396	0.5531	0.5424

TABLE II: UCIQE

\uparrow

performance across different training (UIEB&LSUI) and validation (U45&RUIE) sets. Best results are in bold .

Camera	(A)	(B)	(C)	(D)	(E)	(F)	Ours
Canon D10	9.96	9.62	7.41	7.01	6.43	6.37	5.89
Fujifilm Z33	8.23	12.01	14.03	8.00	6.25	10.89	10.77
Olympus T6000	7.71	10.49	11.12	7.40	12.97	10.10	7.62
Olympus T8000	11.14	6.90	8.48	10.43	9.53	8.34	9.03
Panasonic TS1	9.81	12.01	10.89	17.52	14.41	12.29	13.95
Pentax W60	7.87	8.19	7.10	8.63	7.15	6.70	6.50

TABLE III: CIEDE2000

\downarrow

values across different camera models. Best results are in bold . (A) U-Shape [ 32 ] , (B) Lite [ 49 ] , (C) Boths [ 50 ] , (D) Phaseformer [ 34 ] , (E) SFGNet [ 48 ] , (F) MobileIE [ 43 ] .

Fig. 6: Visual comparison results on U45 and RUIE. From top to bottom, the rows represent inference configurations (Training $\rightarrow$ Testing): (1) UIEB $\rightarrow$ U45, (2) LSUI $\rightarrow$ U45, (3) UIEB $\rightarrow$ RUIE, and (4) LSUI $\rightarrow$ RUIE. Method: (A) U-shape, (B) WaterNet, (C) SFGNet, (D) Lite, and (E) MobileIE (please zoom in for better visualization).

Fig. 7: Qualitative results on diverse benchmarks. The bottom-right corners display the reference color checker for Colorcheck7 dataset.

Method	AWCC	MRDConv	SGCA	PSNR $\uparrow$	SSIM $\uparrow$
Baseline				22.227	0.891
(A)		$\checkmark$	$\checkmark$	22.518	0.881
(B)	$\checkmark$		$\checkmark$	23.686	0.886
(C)	$\checkmark$	$\checkmark$		23.370	0.894
Full	$\checkmark$	$\checkmark$	$\checkmark$	24.055	0.902

TABLE IV: Ablation analysis of the three components.

Fig. 8: Visual ablation results of the proposed modules. The bottom row displays the corresponding sRGB color space point cloud distributions, illustrating the progressive correction of color degradation.

Method	$\mu_{c}$	$\sigma_{c}$	$v_{bright}$	$v_{dark}$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	UIQM $\uparrow$
(A)		$\checkmark$	$\checkmark$	$\checkmark$	24.034	0.898	0.123	3.055
(B)	$\checkmark$		$\checkmark$	$\checkmark$	24.007	0.899	0.122	3.078
(C)	$\checkmark$	$\checkmark$		$\checkmark$	23.721	0.894	0.128	3.090
(D)	$\checkmark$	$\checkmark$	$\checkmark$		23.411	0.891	0.128	3.029
Full	$\checkmark$	$\checkmark$	$\checkmark$	$\checkmark$	24.055	0.902	0.121	3.045

TABLE V: Ablation study of different components in SGCA.

Fig. 9: Visual comparison of different components in the SGCA module.

Method	$\mathcal{L}_{char}$	$\mathcal{L}_{psnr}$	$\mathcal{L}_{vgg}$	$\mathcal{L}_{color}$	PSNR $\uparrow$	SSIM $\uparrow$
Baseline	$\checkmark$				23.133	0.891
(A)	$\checkmark$		$\checkmark$	$\checkmark$	23.522	0.890
(B)	$\checkmark$	$\checkmark$		$\checkmark$	23.961	0.890
(C)	$\checkmark$	$\checkmark$	$\checkmark$		24.014	0.886
Full	$\checkmark$	$\checkmark$	$\checkmark$	$\checkmark$	24.055	0.902

TABLE VI: Ablation study of different loss components.

Fig. 10: Visual ablation results of different loss functions (please zoom in for better visualization).

Method	Params	FLOPs	FPS	PSNR $\uparrow$	SSIM $\uparrow$
Method	(K)	(G)	$640\times 480$	PSNR $\uparrow$	SSIM $\uparrow$
Rep-scale=4	3.88	0.145	409.09	24.055	0.902
Rep-scale=8	10.87	0.686	326.15	24.406	0.904

TABLE VII: Efficiency and performance analysis for MRDConv.

Fig. 11: Instance segmentation results on the UIIS dataset. Compared with other methods, our model not only enhances overall visual quality, but also preserves sharper boundaries and finer structural details, producing more accurate mask predictions for categories such as Human Divers and Aquatic Plants.

Method	Fish	Reefs	Aquatic Plants	Wrecks& Ruins	Human Divers	Robots	Sea-floor	mAP $\uparrow$
WaterMask [ 57 ]	0.442	0.260	0.076	0.184	0.473	0.075	0.066	0.225
Boths [ 50 ]	0.438	0.262	0.074	0.215	0.442	0.134	0.065	0.233
FiveA+ [ 47 ]	0.434	0.256	0.078	0.192	0.478	0.143	0.054	0.234
Lite [ 49 ]	0.432	0.261	0.074	0.192	0.458	0.120	0.063	0.229
MobileIE [ 43 ]	0.438	0.254	0.072	0.184	0.472	0.138	0.060	0.231
Ours	0.440	0.263	0.079	0.193	0.478	0.135	0.067	0.237

TABLE VIII: Instance segmentation performance (mAP) across different enhancement methods on the UIIS dataset.

NTU	Illum.	Raw				Enhanced				$\Delta\%$
NTU	Illum.	$\sigma_{c}$	${con}_{l}$	$\mu_{s}$	UCIQE $\uparrow$	$\sigma_{c}$	${con}_{l}$	$\mu_{s}$	UCIQE $\uparrow$	$\Delta\%$
0.55	I	0.189	0.624	0.836	0.475	0.287	0.926	0.842	0.605	27.3
0.55	II	0.187	0.628	0.833	0.474	0.270	0.949	0.841	0.604	27.3
0.99	I	0.162	0.545	0.815	0.435	0.288	0.926	0.852	0.608	39.7
0.99	II	0.161	0.557	0.814	0.438	0.262	0.941	0.849	0.600	36.9
2.41	I	0.183	0.537	0.782	0.435	0.279	0.949	0.839	0.607	39.7

TABLE IX: Quantitative quality assessment (UCIQE) under varying turbidity and illumination intensity.

Fig. 12: Visual results from controlled laboratory tank experiments under varying turbidity levels and illumination intensities, demonstrating the robustness of our method in diverse turbid and lighting conditions.

Fig. 13: Downstream task performance. After enhancement, the model enables precise background segmentation of pool boundaries and successful detection of the toy submarine, which are obscured in the raw frames. For the pool experiments, the stereo camera core is enclosed in housings designed for deep-sea environments.

Fig. 14: Real-world deployment. ROV operation resuspends bottom sediment, causing turbidity and reduced visibility (about 15 m depth, this condition does not correspond to the clear-water state of 2-3 NTU). For the Real-world experiments, the same stereo camera core is used without the deep-sea housings. The SIFT analysis shows that our method significantly increases the number of stable keypoint correspondences between consecutive frames.

Explore similar work

Real-Time Underwater Image Enhancement via Frequency-Guided Dual-Path Attention
Jun 29, 2026Leshen Zhang, Ao Li, Ce ZhuImage Enhancement Underwater Image Enhancement
Glasgow College, UESTC, Chengdu, China · University of Glasgow, Glasgow, UK · School of Information and Communication Engineering, UESTC, Chengdu, China
A Dual-Branch Collaborative Framework for Joint Optimization of Underwater Image Enhancement and Object Detection
Jun 14, 2026Liyuan Cao, Zheng Liu, Guanghao Liao +2Underwater Image Enhancement Underwater Imaging
School of Electronic and Information Engineering, University of Science and Technology Liaoning, No.189 Qianshan Middle Road, Lishan District, Anshan, 114051, Liaoning, China
Physics-Guided Spectral Distillation for Underwater Image Enhancement on Resource-Constrained Devices
Sep 28, 2026Yifan Chen, Kai He, Ye Zheng +3Image Enhancement Underwater Image Enhancement
College of Future Information Technology, Fudan University, Shanghai 200433, China · Institute of Artificial Intelligence (TeleAI), China Telecom, China · School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, China

Jun 29, 2026J/K move · Enter open · S save

Real-Time Underwater Image Enhancement via Frequency-Guided Dual-Path Attention

Leshen Zhang, Ao Li, Ce Zhu

Glasgow College, UESTC, Chengdu, China · University of Glasgow, Glasgow, UK · School of Information and Communication Engineering, UESTC, Chengdu, China

Image Enhancement Underwater Image Enhancement Frequency-Domain Image Processing Convolutional Neural Networks Lightweight CNNs

Open paper AskPDF arXiv

Real-time underwater image enhancement (UIE) is crucial for mobile underwater photography and autonomous robotic systems, where practical deployment typically requires low latency and compact models under constrained computational resources. Recent ultra-lightweight CNNs based on structural re-parameterization meet these constraints but operate purely in the spatial domain, ignoring the frequency-sensitive nature of underwater degradation. To address this, we propose a lightweight UIE framework that integrates two key components: a Multi-Branch Reparameterizable Convolution with Fixed DCT Priors (MBRConv-DCT) that injects structured directional frequency priors during training, and a Frequency-Guided Dual-Path Attention (FGDPA) module that fuses spatial and spectral cues via a dual-path design for adaptive feature modulation. Both components are fully compatible with structural re-parameterization: the convolution branch introduces zero additional inference cost after re-parameterization, while the attention module incurs only a minimal computational overhead. Experiments show our model achieves state-of-the-art performance with only 4.23K parameters and 600+ FPS, outperforming much larger methods in both quantitative metrics and visual quality. Code is available at https://github.com/LethyZhang/FGDPA.

Abstract

Explore similar work

Real-Time Underwater Image Enhancement via Frequency-Guided Dual-Path Attention

A Dual-Branch Collaborative Framework for Joint Optimization of Underwater Image Enhancement and Object Detection

Physics-Guided Spectral Distillation for Underwater Image Enhancement on Resource-Constrained Devices