Multi-stage dynamic wavelet gated aggregation network for underwater image denoising

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Abstract

Underwater image quality is inevitably degraded by the selective absorption and scattering of light, which introduce severe blurring and abrupt high-frequency noise. These interferences obscure latent structural information and make robust restoration a significant challenge. To address this, we present MWGANet, a multi-stage dynamic wavelet-gated aggregation network designed to decouple complex underwater noise distributions caused by environmental disturbances. The core of our architecture was a parallel strategy with three pathways tailored for feature extraction across domains. Specifically, the Gated Wavelet Transform Branch (GWTB) isolated noise spikes in the frequency domain to effectively safeguard critical structural textures from corruption. Our adaptive reuse branch (ARB) incorporated a dynamic aggregation block at multiple scales to facilitate feature flow across layers while adaptively filtering redundant information through learnable gating mechanisms. Furthermore, the dilated context branch (DCB) expanded the model’s receptive field via graduated dilation rates to enhance the perception of diverse object priors. Extensive evaluations confirmed that MWGANet achieves a superior balance between denoising fidelity and computational efficiency. In particular, the model maintained a high-fidelity output on the EUVP dataset, achieving a PSNR of 31.06 dB even under severe synthetic additive white Gaussian noise (AWGN) (σ = 50). Such robustness and efficiency indicated that MWGANet has the potential to serve as a practical real-time solution for resource-constrained autonomous underwater vehicle (AUV) platforms.

Keywords

underwater image denoising / wavelet transform / attention mechanism / multi-scale aggregation / feature reuse

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References

[1] D. Akkaynak, T. Treibitz, A revised underwater image formation model, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 6723-6732. https://doi.org/10.1109/CVPR.2018.00703

[2] C. Li, W. Liu, G. Gong, X. Ding, X. Zhong, SU-YOLO: Spiking neural network for efficient underwater object detection, Neurocomputing, 644 (2025), 130310. https://doi.org/10.1016/j.neucom.2025.130310

[3] J. S. Jaffe, Computer modeling and the design of optimal underwater imaging systems, IEEE J. Ocean. Eng., 15 (1990), 101-111. https://doi.org/10.1109/48.50695

[4] R. Schettini, S. Corchs, Underwater image processing: State of the art of restoration and image enhancement methods, EURASIP J. Adv. Signal Process., 2010 (2010), 746052. https://doi.org/10.1155/2010/746052

[5] C. Li, C. Guo, W. Ren, R. Cong, J. Hou, S. Kwong, et al., An underwater image enhancement benchmark dataset and beyond, IEEE Trans. Image Process., 29 (2020), 4376-4389. https://doi.org/10.1109/TIP.2019.2955241

[6] S. Anwar, C. Li, Diving deeper into underwater image enhancement: A survey, Signal Process.: Image Commun., 89 (2020), 115978. https://doi.org/10.1016/j.image.2020.115978

[7] K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process., 26 (2017), 3142-3155. https://doi.org/10.1109/TIP.2017.2662206

[8] Y. Blau, T. Michaeli, The perception-distortion tradeoff, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 6228-6237. https://doi.org/10.1109/CVPR.2018.00652

[9] A. Foi, M. Trimeche, V. Katkovnik, K. Egiazarian, Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data, IEEE Trans. Image Process., 17 (2008), 1737-1754. https://doi.org/10.1109/TIP.2008.2001399

[10] S. Guo, Z. Yan, K. Zhang, W. Zuo, L. Zhang, Toward convolutional blind denoising of real photographs, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2019), 1712-1722. https://doi.org/10.1109/CVPR.2019.00182

[11] C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, C. W. Lin, Deep learning on image denoising: An overview, Neural Networks, 131 (2020), 251-275. https://doi.org/10.1016/j.neunet.2020.07.025

[12] A. Buades, B. Coll, J. M. Morel, A non-local algorithm for image denoising, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) , (2005), 60-65. https://doi.org/10.1109/CVPR.2005.38

[13] K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process., 16 (2007), 2080-2095. https://doi.org/10.1109/TIP.2007.901238

[14] S. Gu, L. Zhang, W. Zuo, X. Feng, Weighted nuclear norm minimization with application to image denoising, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, (2014), 2862-2869. https://doi.org/10.1109/CVPR.2014.366

[15] J. Mairal, F. Bach, J. Ponce, G. Sapiro, A. Zisserman, Non-local sparse models for image restoration, in 2009 IEEE 12th International Conference on Computer Vision , (2009), 2272-2279. https://doi.org/10.1109/ICCV.2009.5459452

[16] S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M. H. Yang, Restormer: Efficient transformer for high-resolution image restoration, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2022), 5728-5739. https://doi.org/10.1109/CVPR52688.2022.00564

[17] Z. Wang, X. Cun, J. Bao, W. Zhou, J. Liu, H. Li, Uformer: A general U-shaped transformer for image restoration, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2022), 17683-17693. https://doi.org/10.1109/CVPR52688.2022.01716

[18] S. G. Mallat, A theory for multiresolution signal decomposition: The wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell., 11 (1989), 674-693. https://doi.org/10.1109/34.192463

[19] D. L. Donoho, De-noising by soft-thresholding, IEEE Trans. Inf. Theory, 41 (1995), 613-627. https://doi.org/10.1109/18.382009

[20] S. G. Chang, B. Yu, M. Vetterli, Adaptive wavelet thresholding for image denoising and compression, IEEE Trans. Image Process., 9 (2000), 1532-1546. https://doi.org/10.1109/83.862633

[21] J. Portilla, V. Strela, M. J. Wainwright, E. P. Simoncelli, Image denoising using scale mixtures of Gaussians in the wavelet domain, IEEE Trans. Image Process., 12 (2003), 1338-1351. https://doi.org/10.1109/TIP.2003.818640

[22] C. J. Prabhakar, P. U. P. Kumar, An image based technique for enhancement of underwater images, Int. J. Mach. Intell., 3 (2010), 217-224.

[23] M. Jian, X. Liu, H. Luo, X. Lu, H. Yu, J. Dong, Underwater image processing and analysis: A review, Signal Process. Image Commun., 91 (2021), 116088. https://doi.org/10.1016/j.image.2020.116088

[24] P. Liu, H. Zhang, K. Zhang, L. Lin, W. Zuo, Multi-level wavelet-CNN for image restoration, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (2018), 773-782. https://doi.org/10.1109/CVPRW.2018.00121

[25] H. Huang, R. He, Z. Sun, T. Tan, Wavelet-SRNet: A wavelet-based CNN for multi-scale face super resolution, in 2017 IEEE International Conference on Computer Vision (ICCV), (2017), 1689-1697. https://doi.org/10.1109/ICCV.2017.187

[26] K. Jiang, Z. Wang, P. Yi, C. C. Wang, Z. Wang, Z. Han, et al., Direction-aware attention wavelet network for image deraining, in Proceedings of the 31st ACM International Conference on Multimedia, (2023), 7065-7074. https://doi.org/10.1145/3581783.3611697

[27] Y. Rao, W. Zhao, Y. Tang, J. Zhou, S. N. Lim, J. Lu, HorNet: Efficient high-order spatial interactions with recursive gated convolutions, Adv. Neural Inf. Process. Syst., 35 (2022), 10353-10366.

[28] M. H. Guo, C. Z. Lu, Z. N. Liu, M. M. Cheng, S. M. Hu, Visual attention network, Comput. Vis. Media, 9 (2023), 733-752. https://doi.org/10.1007/s41095-023-0364-2

[29] S. K. Li, X. Wang, H. Zuo, C. Qian, S. Pu, M. Li, MogaNet: Multi-order gated aggregation network, in International Conference on Learning Representations, (2024), 37393-37427.

[30] X. Li, W. Wang, X. Hu, J. Yang, Selective kernel networks, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2019), 510-519. https://doi.org/10.1109/CVPR.2019.00060

[31] H. Zhang, C. Wu, Z. Zhang, Y. Zhu, H. Lin, Z. Zhang, et al., ResNeSt: Split-attention networks, IEEE Trans. Pattern Anal. Mach. Intell., 44 (2022), 5765-5776. https://doi.org/10.1109/CVPRW56347.2022.00309

[32] B. Yang, G. Bender, Q. V. Le, J. Ngiam, CondConv: Conditionally parameterized convolutions for efficient inference, preprint, arXiv:1904.04971.

[33] C. Li, A. Zhou, A. Yao, Omni-dimensional dynamic convolution, preprint, arXiv:2209.07947.

[34] J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), 3431-3440. https://doi.org/10.1109/CVPR.2015.7298965

[35] G. Huang, Z. Liu, L. van der Maaten, K. Q. Weinberger, Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 4700-4708. https://doi.org/10.1109/CVPR.2017.243

[36] J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 7132-7141. https://doi.org/10.1109/CVPR.2018.00745

[37] S. Woo, J. Park, J. Y. Lee, I. S. Kweon, CBAM: Convolutional block attention module, in Computer Vision - ECCV 2018, (2018), 3-19. https://doi.org/10.1007/978-3-030-01234-2_1

[38] Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image super-resolution, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 2472-2481. https://doi.org/10.1109/CVPR.2018.00262

[39] Q. Jiang, Y. Chen, G. Wang, T. Ji, A novel deep neural network for noise removal from underwater image, Signal Process. Image Commun., 87 (2020), 115921. https://doi.org/10.1016/j.image.2020.115921

[40] H. Sheng, X. Wang, C. Zhang, J. Wang, P. Duan, Y. Wang, AIGC video detection based on the fusion of spatial-frequency-optical flow multimodal features, J. Syst. Eng. Electron., 36 (2026), 1-15. https://doi.org/10.23919/JSEE.2026.000049

[41] H. Zhao, O. Gallo, I. Frosio, J. Kautz, Loss functions for image restoration with neural networks, IEEE Trans. Comput. Imaging, 3 (2016), 47-57. https://doi.org/10.1109/TCI.2016.2644865

[42] J. Yang, H. Xie, N. Xue, A. Zhang, Research on underwater image denoising based on dual-channel residual network, Comput. Eng., 49 (2023), 188-198. https://doi.org/10.19678/j.issn.1000-3428.0064662

[43] M. J. Islam, Y. Xia, J. Sattar, Simultaneous enhancement and super-resolution of underwater imagery for improved visual perception, in Robotics: Science and Systems (RSS), 2020. https://doi.org/10.15607/RSS.2020.XVI.018

[44] M. J. Islam, Y. Xia, J. Sattar, Fast underwater image enhancement for improved visual perception, IEEE Robot. Autom. Lett., 5 (2020), 3227-3234. https://doi.org/10.1109/LRA.2020.2974710

[45] R. Timofte, E. Agustsson, L. Van Gool, M. Yang, L. Zhang, B. Lim, et al., NTIRE 2017 challenge on single image super-resolution: Dataset and study, in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (2017) 1122-1131. https://doi.org/10.1109/CVPRW.2017.149

[46] R. Franzen, Kodak lossless true color image suite, 1999.

[47] D. Martin, C. Fowlkes, D. Tal, J. Malik, A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, (2001), 416-423. https://doi.org/10.1109/ICCV.2001.937655

[48] T. Q. Huynh-Thu, M. Ghanbari, Scope of validity of PSNR in image/video quality assessment, Electron. Lett., 44 (2008), 800-801. https://doi.org/10.1049/el:20080522

[49] Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 13 (2004), 600-612. https://doi.org/10.1109/TIP.2003.819861

[50] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, O. Wang, The unreasonable effectiveness of deep features as a perceptual metric, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 586-595. https://doi.org/10.1109/CVPR.2018.00068

[51] C. Tian, Y. Xu, W. Zuo, L. Fei, C. W. Lin, Attention-guided CNN denoising network (ADNet), Neural Networks, 121 (2020), 461-473. https://doi.org/10.1016/j.neunet.2019.12.024

[52] C. Tian, Y. Xu, L. Fei, J. Wang, J. Wen, N. Luo, Enhanced CNN for image denoising, CAAI Trans. Intell. Technol., 4 (2019), 17-23. https://doi.org/10.1049/trit.2018.1054

[53] C. Tian, Y. Xu, W. Zuo, B. Du, C. W. Lin, D. Zhang, Designing and training of a dual CNN for image denoising, Knowl.-Based Syst., 226 (2021), 107145. https://doi.org/10.1016/j.knosys.2021.106949

[54] I. Batool, M. Imran, A dual residual dense network for image denoising, Eng. Appl. Artif. Intell., 147 (2025), 110275. https://doi.org/10.1016/j.engappai.2025.110275

[55] C. Tian, M. Zheng, W. Zuo, B. Zhang, Y. Zhang, D. Zhang, Multi-stage image denoising with the wavelet transform, Pattern Recognit., 134 (2023), 109050. https://doi.org/10.1016/j.patcog.2022.109050