{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:07:43Z","timestamp":1778821663934,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Underwater images often suffer from mixed degradations, including motion blur, which reduce structural clarity and adversely affect downstream vision tasks. To address this problem, we propose a physically guided Transformer framework for underwater motion deblurring. The proposed method combines two-stage cepstrum-based blur estimation with a point spread function (PSF)-guided self-attention mechanism. Specifically, blur parameters are first robustly estimated through cepstrum analysis, ellipse fitting, and negative-peak refinement, and the resulting PSF is then embedded into the Transformer attention module to guide feature aggregation. On the real underwater benchmark datasets UIEB Challenge-60 and EUVP330, the proposed method achieves UIQM\/UCIQE scores of 4.09\/0.56 and 3.40\/0.58, respectively, significantly outperforming UFPNet and Phaseformer, thereby demonstrating superior perceptual restoration in terms of sharpness, contrast, and color consistency. On the synthetic test set, the proposed method attains 24.23 dB PSNR and 0.918 SSIM, outperforming both recent deep models and classical non-blind deconvolution methods, which confirms its strong restoration fidelity and structural consistency. In the controlled water-tank experiments, the proposed method consistently achieves the best performance under different camera motion speeds, demonstrating excellent robustness and practical applicability. Overall, the proposed framework provides an effective and physically interpretable solution for underwater motion deblurring.<\/jats:p>","DOI":"10.3390\/jimaging12050186","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T08:12:31Z","timestamp":1777363951000},"page":"186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physically Guided Attention Mechanism for Underwater Motion Deblurring via Cepstrum-Based Blur Estimation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4841-0354","authenticated-orcid":false,"given":"Ning","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37916, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Environmental Engineering Sciences, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jindong","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37916, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TIP.2017.2663846","article-title":"Underwater Image Restoration Based on Image Blurriness and Light Absorption","volume":"26","author":"Peng","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TIP.2017.2759252","article-title":"Color Balance and Fusion for Underwater Image Enhancement","volume":"27","author":"Ancuti","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120896","DOI":"10.1016\/j.oceaneng.2025.120896","article-title":"An underwater visual SLAM system with adaptive image enhancement","volume":"326","author":"Chen","year":"2025","journal-title":"Ocean Eng."},{"key":"ref_4","first-page":"5011811","article-title":"EUM-SLAM: An Enhancing Underwater Monocular Visual SLAM with Deep-Learning-Based Optical Flow Estimation","volume":"74","author":"Wang","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1109\/TSP.2006.870644","article-title":"Efficient discrete spatial techniques for blur support identification in blind image deconvolution","volume":"54","author":"Chen","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_6","first-page":"636","article-title":"Generalized Wiener Filtering Computation Techniques","volume":"100","author":"Pratt","year":"2006","journal-title":"IEEE Trans. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Krishnan, D., Tay, T., and Fergus, R. (2011, January 20\u201325). Blind Deconvolution Using a Normalized Sparsity Measure. Proceedings of the CVPR, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995521"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"141789","DOI":"10.1109\/ACCESS.2023.3339817","article-title":"Depth-Conditioned GAN for Underwater Image Enhancement","volume":"11","author":"Wu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_9","unstructured":"Cong, X., Zhao, Y., Gui, J., Hou, J., and Tao, D. (2024). A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., and Matas, J. (2018, January 18\u201323). Deblurgan: Blind motion deblurring using conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00854"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nah, S., Kim, T.H., and Lee, K.M. (2017, January 21\u201326). Deep multi-scale convolutional neural network for dynamic scene deblurring. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.35"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cho, S.J., Ji, S.W., Hong, J.P., Jung, S.W., and Ko, S.J. (2021, January 10\u201317). Rethinking coarse-to-fine approach in single image deblurring. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00460"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., and Yang, M.H. (2022, January 18\u201324). Restormer: Efficient transformer for high-resolution image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"ref_14","unstructured":"Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., and Li, H. (, January 18\u201324). Uformer: A general u-shaped transformer for image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., and Shao, L. (2021, January 20\u201325). Multi-stage progressive image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"ref_16","unstructured":"Zhao, C., Dong, C., and Cai, W. (2024). Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nie, X., Pan, S., Zhai, X., Tao, S., Qu, F., Wang, B., Ge, H., and Xiao, G. (2024). Image-Conditional Diffusion Transformer for Underwater Image Enhancement. arXiv.","DOI":"10.2139\/ssrn.5227958"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, L., and Jia, J. (2010). Two-Phase Kernel Estimation for Robust Motion Deblurring. European Conference on Computer Vision, Springer. Available online: http:\/\/www.cse.cuhk.edu.hk\/.","DOI":"10.1007\/978-3-642-15549-9_12"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., and Freeman, W.T. (2010). Removing Camera Shake from a Single Photograph. ACM Siggraph 2006 Papers, ACM.","DOI":"10.1145\/1179352.1141956"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Levin, A., Fergus, R., Durand, F., and Freeman, W.T. (2009, January 20\u201325). Understanding and evaluating blind deconvolution algorithms. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206815"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1086\/111605","article-title":"An iterative technique for the rectification of observed distributions","volume":"79","author":"Lucy","year":"1974","journal-title":"Astron. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1145\/1276377.1276464","article-title":"Image and depth from a conventional camera with a coded aperture","volume":"26","author":"Levin","year":"2007","journal-title":"ACM Trans. Graph."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e36151","DOI":"10.1016\/j.heliyon.2024.e36151","article-title":"Image restoration model for microscopic defocused images based on blurring kernel guidance","volume":"10","author":"Wei","year":"2024","journal-title":"Heliyon"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jia, J. (2007, January 17\u201322). Single Image Motion Deblurring Using Transparency. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383029"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Park, J., Kim, M., Chang, S., and Lee, K.H. (2011, January 14\u201317). Estimation of motion blur parameters using cepstrum analysis. Proceedings of the 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE), Singapore.","DOI":"10.1109\/ISCE.2011.5973859"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"855","DOI":"10.4028\/www.scientific.net\/AMM.608-609.855","article-title":"Parameter estimation and restoration of motion blurred image","volume":"608","author":"Song","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"174204","DOI":"10.7498\/aps.62.174204","article-title":"PSF estimation via gradient cepstrum analysis for single blurred image","volume":"62","author":"Shi","year":"2013","journal-title":"Wuli Xuebao\/Acta Phys. Sin."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fang, Z., Wu, F., Dong, W., Li, X., Wu, J., and Shi, G. (2023, January 17\u201324). Self-supervised non-uniform kernel estimation with flow-based motion prior for blind image deblurring. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01736"},{"key":"ref_29","unstructured":"Khan, R., Negi, A., Kulkarni, A., Phutke, S.S., Vipparthi, S.K., and Murala, S. (March, January 26). Phaseformer: Phase-based attention mechanism for underwater image restoration and beyond. Proceedings of the Winter Conference on Applications of Computer Vision, Tucson, AZ, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4376","DOI":"10.1109\/TIP.2019.2955241","article-title":"An underwater image enhancement benchmark dataset and beyond","volume":"29","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3227","DOI":"10.1109\/LRA.2020.2974710","article-title":"Fast underwater image enhancement for improved visual perception","volume":"5","author":"Islam","year":"2020","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/186\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:11:25Z","timestamp":1778818285000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,26]]},"references-count":31,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jimaging12050186"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12050186","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,26]]}}}