{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:46:36Z","timestamp":1770813996652,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Special Projects in universities' key fields of Guangdong Province","award":["2023ZDZX3017"],"award-info":[{"award-number":["2023ZDZX3017"]}]},{"name":"Guangdong Key Discipline Research Enhancement Project","award":["(20240ZDJS055"],"award-info":[{"award-number":["(20240ZDJS055"]}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["No. 2025A1515012983"],"award-info":[{"award-number":["No. 2025A1515012983"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00530-025-02114-8","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T04:29:47Z","timestamp":1765772987000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-scale fusion framework for underwater image enhancement based on fourier stabilization and dynamic sparse transformer"],"prefix":"10.1007","volume":"32","author":[{"given":"Dan","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"2114_CR1","doi-asserted-by":"publisher","unstructured":"Wang, H., Zhang, W., Ren, P.: Self-organized underwater image enhancement. ISPRS J. Photogrammetry Remote Sens. 215, 1\u201314 (2024). https:\/\/doi.org\/10.1016\/j.isprsjprs.2024.06.019","DOI":"10.1016\/j.isprsjprs.2024.06.019"},{"key":"2114_CR2","doi-asserted-by":"publisher","first-page":"69356","DOI":"10.1109\/ACCESS.2023.3290201","volume":"11","author":"D Xiang","year":"2023","unstructured":"Xiang, D., Wang, H., He, D., Zhai, C.: Research on histogram equalization algorithm based on optimized adaptive quadruple segmentation and cropping of underwater image (AQSCHE). IEEE Access. 11, 69356\u201369365 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3290201","journal-title":"IEEE Access."},{"key":"2114_CR3","doi-asserted-by":"publisher","first-page":"5442","DOI":"10.1109\/TIP.2022.3196546","volume":"31","author":"P Zhuang","year":"2022","unstructured":"Zhuang, P., Wu, J., Porikli, F., Li, C.: Underwater image enhancement with Hyper-Laplacian reflectance priors. IEEE Trans. Image Process. 31, 5442\u20135455 (2022). https:\/\/doi.org\/10.1109\/TIP.2022.3196546","journal-title":"IEEE Trans. Image Process."},{"key":"2114_CR4","doi-asserted-by":"publisher","unstructured":"Song, Y., She, M., K\u00f6ser, K.: Advanced underwater image restoration in complex illumination conditions. ISPRS J. Photogrammetry Remote Sens. 209, 197\u2013212 (2024). https:\/\/doi.org\/10.1016\/j.isprsjprs.2024.02.004","DOI":"10.1016\/j.isprsjprs.2024.02.004"},{"key":"2114_CR5","doi-asserted-by":"publisher","unstructured":"Bianco, G., Muzzupappa, M., Bruno, F., Garcia, R., Neumann, L., A new color correction method, for underwater imaging: Int. Arch. Photogramm Remote Sens. Spat. Inf. Sci. XL\u20135\/W5, 25\u201332 (2015). https:\/\/doi.org\/10.5194\/isprsarchives-XL-5-W5-25-2015","DOI":"10.5194\/isprsarchives-XL-5-W5-25-2015"},{"key":"2114_CR6","doi-asserted-by":"publisher","unstructured":"Zhang, W., et al.: Underwater image enhancement via weighted wavelet visual perception fusion. IEEE Trans. Circuits Syst. Video Technol. 34(4), 2469\u20132483 (2024). https:\/\/doi.org\/10.1109\/TCSVT.2023.3299314","DOI":"10.1109\/TCSVT.2023.3299314"},{"key":"2114_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2025.3585119","volume":"63","author":"H Li","year":"2025","unstructured":"Li, H., Li, L., Wang, H., Zhang, W., Ren, P.: Underwater image captioning with AquaSketch-Enhanced Cross-Scale information fusion. IEEE Trans. Geosci. Remote Sens. 63, 1\u201318 (2025). https:\/\/doi.org\/10.1109\/TGRS.2025.3585119","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2114_CR8","doi-asserted-by":"publisher","unstructured":"Wang, H., Sun, S., Ren, P.: Underwater color disparities: Cues for enhancing underwater images toward natural color consistencies. IEEE Trans. Circuits Syst. Video Technol. 34(2), 738\u2013753 (2024). https:\/\/doi.org\/10.1109\/TCSVT.2023.3289566","DOI":"10.1109\/TCSVT.2023.3289566"},{"key":"2114_CR9","doi-asserted-by":"publisher","unstructured":"Wang, H., Frery, A.C., Li, M., Ren, P.: Underwater image enhancement via histogram similarity-oriented color compensation complemented by multiple attribute adjustment. Intell. Mar. Technol. Syst. 1(1), 12 (2023). https:\/\/doi.org\/10.1007\/s44295-023-00015-y","DOI":"10.1007\/s44295-023-00015-y"},{"key":"2114_CR10","doi-asserted-by":"publisher","first-page":"3066","DOI":"10.1109\/TIP.2023.3276332","volume":"32","author":"L Peng","year":"2023","unstructured":"Peng, L., Zhu, C., Bian, L.: U-Shape transformer for underwater image enhancement. IEEE Trans. Image Process. 32, 3066\u20133079 (2023). https:\/\/doi.org\/10.1109\/TIP.2023.3276332","journal-title":"IEEE Trans. Image Process."},{"key":"2114_CR11","doi-asserted-by":"publisher","unstructured":"Wang, H., INSPIRATION, et al.: A reinforcement learning-based human visual perception-driven image enhancement paradigm for underwater scenes. Eng. Appl. Artif. Intell. 133, 108411 (2024). https:\/\/doi.org\/10.1016\/j.engappai.2024.108411","DOI":"10.1016\/j.engappai.2024.108411"},{"key":"2114_CR12","doi-asserted-by":"publisher","unstructured":"He, K., Sun, J., Tang, X.: c, IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341\u20132353, (2011). https:\/\/doi.org\/10.1109\/TPAMI.2010.168","DOI":"10.1109\/TPAMI.2010.168"},{"key":"2114_CR13","doi-asserted-by":"publisher","unstructured":"Kang, L.-W., Lin, C.-W., Fu, Y.-H.: Automatic Single-Image-Based rain streaks removal via image decomposition. IEEE Trans. Image Process. 21(4), 1742\u20131755 (2012). https:\/\/doi.org\/10.1109\/TIP.2011.2179057","DOI":"10.1109\/TIP.2011.2179057"},{"key":"2114_CR14","doi-asserted-by":"publisher","unstructured":"Wang, R., Wang, Y., Zhang, J., Fu, X.: Review on underwater image restoration and enhancement algorithms, in Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, Zhangjiajie Hunan China: ACM. pp. 1\u20136. (2015). https:\/\/doi.org\/10.1145\/2808492.2808548","DOI":"10.1145\/2808492.2808548"},{"key":"2114_CR15","doi-asserted-by":"publisher","unstructured":"Trucco, E., Olmos-Antillon, A.T.: Self-Tuning Underwater Image Restoration, IEEE J. Oceanic Eng., vol. 31, no. 2, pp. 511\u2013519, (2006). https:\/\/doi.org\/10.1109\/JOE.2004.836395","DOI":"10.1109\/JOE.2004.836395"},{"key":"2114_CR16","doi-asserted-by":"publisher","unstructured":"Tansey, W., Thomason, J., Scott, J.: Maximum-Variance total variation denoising for interpretable Spatial smoothing. AAAI. 32(1) (2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11893","DOI":"10.1609\/aaai.v32i1.11893"},{"key":"2114_CR17","doi-asserted-by":"publisher","first-page":"5567","DOI":"10.1007\/978-3-642-02256-2_41","volume-title":"Scale Space and Variational Methods in Computer Vision","author":"X-C Tai","year":"2009","unstructured":"Tai, X.-C., Borok, S., Hahn, J.: Image denoising using TV-Stokes equation with an Orientation-Matching minimization. In: Tai, X.-C., M\u00f8rken, K., Lysaker, M., Lie, K.-A. (eds.) in Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, vol. 5567, p. 5567. Springer Berlin Heidelberg, Berlin, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-02256-2_41"},{"key":"2114_CR18","doi-asserted-by":"publisher","unstructured":"Li, F., Li, X., Peng, Y., Li, B., Zhai, Y.: Maximum Information Transfer and Minimum Loss Dehazing for Underwater Image Restoration, IEEE J. Oceanic Eng., vol. 49, no. 2, pp. 622\u2013636r. (2024). https:\/\/doi.org\/10.1109\/JOE.2023.3334478","DOI":"10.1109\/JOE.2023.3334478"},{"key":"2114_CR19","doi-asserted-by":"publisher","first-page":"4470","DOI":"10.1109\/TIP.2025.3586514","volume":"34","author":"L He","year":"2025","unstructured":"He, L., Yi, Z., Liu, J., Chen, C., Lu, M., Chen, Z.: ALSP+: Fast scene recovery via ambient light similarity prior. IEEE Trans. Image Process. 34, 4470\u20134484 (2025). https:\/\/doi.org\/10.1109\/TIP.2025.3586514","journal-title":"IEEE Trans. Image Process."},{"key":"2114_CR20","doi-asserted-by":"publisher","unstructured":"Dong, L., Zhang, W., Xu, W.: Underwater image enhancement via integrated RGB and LAB color models. Sig. Process. Image Commun. 104, 116684 (2022). https:\/\/doi.org\/10.1016\/j.image.2022.116684","DOI":"10.1016\/j.image.2022.116684"},{"key":"2114_CR21","doi-asserted-by":"publisher","unstructured":"Iqbal, K., Odetayo, M., James, A., Salam, R.A., Talib, A.Z.H.: Enhancing the low quality images using Unsupervised Colour Correction Method, in IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey: IEEE. 2010, pp. 1703\u20131709. (2010). https:\/\/doi.org\/10.1109\/ICSMC.2010.5642311","DOI":"10.1109\/ICSMC.2010.5642311"},{"key":"2114_CR22","doi-asserted-by":"publisher","unstructured":"Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27(1), 379\u2013393 (2018). https:\/\/doi.org\/10.1109\/TIP.2017.2759252","DOI":"10.1109\/TIP.2017.2759252"},{"key":"2114_CR23","doi-asserted-by":"publisher","unstructured":"Xiang, D., et al.: Underwater image enhancement based on weighted guided filter image fusion. Multimedia Syst. 30(5), 240 (2024). https:\/\/doi.org\/10.1007\/s00530-024-01432-7","DOI":"10.1007\/s00530-024-01432-7"},{"key":"2114_CR24","doi-asserted-by":"publisher","unstructured":"Xiang, D., et al.: Attenuated color channel adaptive correction and bilateral weight fusion for underwater image enhancement. Opt. Lasers Eng. 184, 108575 (2025). https:\/\/doi.org\/10.1016\/j.optlaseng.2024.108575","DOI":"10.1016\/j.optlaseng.2024.108575"},{"key":"2114_CR25","doi-asserted-by":"publisher","first-page":"7838","DOI":"10.1109\/TMM.2024.3372400","volume":"26","author":"J Zhou","year":"2024","unstructured":"Zhou, J., Wang, S., Lin, Z., Jiang, Q., Sohel, F.: A pixel distribution remapping and Multi-Prior retinex variational model for underwater image enhancement. IEEE Trans. Multimedia. 26, 7838\u20137849 (2024). https:\/\/doi.org\/10.1109\/TMM.2024.3372400","journal-title":"IEEE Trans. Multimedia"},{"key":"2114_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2025.3525962","volume":"63","author":"H Wang","year":"2025","unstructured":"Wang, H., K\u00f6ser, K., Ren, P.: Large foundation model empowered discriminative underwater image enhancement. IEEE Trans. Geosci. Remote Sens. 63, 1\u201317 (2025). https:\/\/doi.org\/10.1109\/TGRS.2025.3525962","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2114_CR27","doi-asserted-by":"publisher","unstructured":"Pan, X., et al.: Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks. Neurocomputing. 229, 88\u201399 (2017). https:\/\/doi.org\/10.1016\/j.neucom.2016.08.103","DOI":"10.1016\/j.neucom.2016.08.103"},{"key":"2114_CR28","doi-asserted-by":"publisher","unstructured":"Pan, X., et al.: Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks. World Wide Web. 21(6), 1721\u20131743 (2018). https:\/\/doi.org\/10.1007\/s11280-017-0520-7","DOI":"10.1007\/s11280-017-0520-7"},{"key":"2114_CR29","doi-asserted-by":"publisher","unstructured":"Hu, H.-M., Guo, Q., Zheng, J., Wang, H., Li, B.: Single image defogging based on illumination decomposition for visual maritime surveillance. IEEE Trans. Image Process. 28(6), 2882\u20132897 (2019). https:\/\/doi.org\/10.1109\/TIP.2019.2891901","DOI":"10.1109\/TIP.2019.2891901"},{"key":"2114_CR30","doi-asserted-by":"publisher","unstructured":"Wang, Y., Zhang, J., Cao, Y., Wang, Z.: A deep CNN method for underwater image enhancement, in IEEE International Conference on Image Processing (ICIP), Beijing: IEEE, Sept. 2017, pp. 1382\u20131386. (2017). https:\/\/doi.org\/10.1109\/ICIP.2017.8296508","DOI":"10.1109\/ICIP.2017.8296508"},{"key":"2114_CR31","doi-asserted-by":"publisher","unstructured":"Fabbri, C., Islam, M.J., Sattar, J.: Enhancing Underwater Imagery Using Generative Adversarial Networks, in IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD: IEEE, May 2018, pp. 7159\u20137165. (2018). https:\/\/doi.org\/10.1109\/ICRA.2018.8460552","DOI":"10.1109\/ICRA.2018.8460552"},{"key":"2114_CR32","doi-asserted-by":"publisher","unstructured":"Zhou, J., Hao, M., Zhang, D., Zou, P., Zhang, W.: Fusion PSPnet Image Segmentation Based Method for Multi-Focus Image Fusion, IEEE Photonics J., vol. 11, no. 6, pp. 1\u201312. (2019). https:\/\/doi.org\/10.1109\/JPHOT.2019.2950949","DOI":"10.1109\/JPHOT.2019.2950949"},{"key":"2114_CR33","doi-asserted-by":"publisher","unstructured":"Li, C., Anwar, S., Porikli, F.: Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn. 98, 107038 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2019.107038","DOI":"10.1016\/j.patcog.2019.107038"},{"key":"2114_CR34","doi-asserted-by":"publisher","unstructured":"Fu, X., Cao, X.: Underwater image enhancement with global\u2013local networks and compressed-histogram equalization. Sig. Process. Image Commun. 86, 115892 (2020). https:\/\/doi.org\/10.1016\/j.image.2020.115892","DOI":"10.1016\/j.image.2020.115892"},{"key":"2114_CR35","doi-asserted-by":"publisher","unstructured":"Xiang, D., et al.: DPMFformer: An underwater image enhancement network based on deep pooling And multi-scale fusion transformer. Earth Sci. Inf. 18(1), 61 (2025). https:\/\/doi.org\/10.1007\/s12145-024-01573-3","DOI":"10.1007\/s12145-024-01573-3"},{"key":"2114_CR36","doi-asserted-by":"publisher","unstructured":"Xiang, D., et al.: A fusion framework with multi-scale Convolution and triple-branch cascaded transformer for underwater image enhancement. Opt. Lasers Eng. 184, 108640 (2025). https:\/\/doi.org\/10.1016\/j.optlaseng.2024.108640","DOI":"10.1016\/j.optlaseng.2024.108640"},{"key":"2114_CR37","doi-asserted-by":"publisher","unstructured":"Xiang, D., He, D., Sun, H., Gao, P., Zhang, J., Ling, J.: HCMPE-Net: An unsupervised network for underwater image restoration with multi-parameter Estimation based on homology constraint. Opt. Laser Technol. 186, 112616 (2025). https:\/\/doi.org\/10.1016\/j.optlastec.2025.112616","DOI":"10.1016\/j.optlastec.2025.112616"},{"key":"2114_CR38","doi-asserted-by":"publisher","unstructured":"Wang, C., Xing, X., Wu, Y., Su, Z., Chen, J.: DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal, in Proceedings of the 28th ACM International Conference on Multimedia, Seattle WA USA: ACM, Oct. pp. 1643\u20131651. (2020). https:\/\/doi.org\/10.1145\/3394171.3413820","DOI":"10.1145\/3394171.3413820"},{"key":"2114_CR39","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) in Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. Lecture Notes in Computer Science, vol. 9351, pp. 234\u2013241. Springer International Publishing, vol. 9351., Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"2114_CR40","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1109\/TIP.2019.2955241","volume":"29","author":"C Li","year":"2020","unstructured":"Li, C., et al.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29, 4376\u20134389 (2020). https:\/\/doi.org\/10.1109\/TIP.2019.2955241","journal-title":"IEEE Trans. Image Process."},{"key":"2114_CR41","doi-asserted-by":"publisher","unstructured":"Islam, M.J., Xia, Y., Sattar, J.: Fast Underwater Image Enhancement for Improved Visual Perception, IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 3227\u20133234. (2020). https:\/\/doi.org\/10.1109\/LRA.2020.2974710","DOI":"10.1109\/LRA.2020.2974710"},{"key":"2114_CR42","doi-asserted-by":"publisher","unstructured":"Islam, M.J., Luo, P., Sattar, J.: Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception, arXiv. (2020). https:\/\/doi.org\/10.48550\/ARXIV.2002.01155","DOI":"10.48550\/ARXIV.2002.01155"},{"key":"2114_CR43","doi-asserted-by":"publisher","unstructured":"Berman, D., Levy, D., Avidan, S., Treibitz, T.: Underwater single image color restoration using Haze-Lines and a new quantitative dataset. IEEE Trans. Pattern Anal. Mach. Intell. 1\u20131 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.2977624","DOI":"10.1109\/TPAMI.2020.2977624"},{"key":"2114_CR44","doi-asserted-by":"publisher","unstructured":"Gao, Z., Yang, J., Zhang, L., Jiang, F., Jiao, X.: Transformer embedded generative adversarial network for underwater image enhancement. Cogn. Comput. 16(1), 191\u2013214 (2024). https:\/\/doi.org\/10.1007\/s12559-023-10197-6","DOI":"10.1007\/s12559-023-10197-6"},{"key":"2114_CR45","doi-asserted-by":"publisher","unstructured":"An, S., Xu, L., Deng, Z., Zhang, H.: HFM: A hybrid fusion method for underwater image enhancement. Eng. Appl. Artif. Intell. 127, 107219 (2024). https:\/\/doi.org\/10.1016\/j.engappai.2023.107219","DOI":"10.1016\/j.engappai.2023.107219"},{"key":"2114_CR46","doi-asserted-by":"publisher","unstructured":"Peng, Y.-T., Cosman, P.C.: Underwater Image Restoration Based on Image Blurriness and Light Absorption, IEEE Trans. on Image Process., vol. 26, no. 4, pp. 1579\u20131594. (2017). https:\/\/doi.org\/10.1109\/TIP.2017.2663846","DOI":"10.1109\/TIP.2017.2663846"},{"key":"2114_CR47","doi-asserted-by":"publisher","unstructured":"Liu, J., Liu, R.W., Sun, J., Zeng, T.: Rank-One prior: Real-Time scene recovery. IEEE Trans. Pattern Anal. Mach. Intell. 45(7), 8845\u20138860 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3226276","DOI":"10.1109\/TPAMI.2022.3226276"},{"key":"2114_CR48","doi-asserted-by":"publisher","unstructured":"Liu, S., Fan, H., Lin, S., Wang, Q., Ding, N., Tang, Y.: Adaptive Learning Attention Network for Underwater Image Enhancement, IEEE Robot. Autom. Lett., vol. 7, no. 2, pp. 5326\u20135333. (2022). https:\/\/doi.org\/10.1109\/LRA.2022.3156176","DOI":"10.1109\/LRA.2022.3156176"},{"key":"2114_CR49","doi-asserted-by":"publisher","first-page":"4472","DOI":"10.1109\/TIP.2023.3286263","volume":"32","author":"R Cong","year":"2023","unstructured":"Cong, R., et al.: Physical Model-Guided underwater image enhancement using GAN with Dual-Discriminators. IEEE Trans. Image Process. 32, 4472\u20134485 (2023). https:\/\/doi.org\/10.1109\/TIP.2023.3286263","journal-title":"IEEE Trans. Image Process."},{"key":"2114_CR50","doi-asserted-by":"publisher","unstructured":"Fu, Z., Lin, X., Wang, W., Huang, Y., Ding, X., Underwater Image Enhancement Via Learning Water Type Desensitized Representations, in: \u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore: IEEE, May 2022, pp. 2764\u20132768. (2022). https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9747758","DOI":"10.1109\/ICASSP43922.2022.9747758"},{"key":"2114_CR51","doi-asserted-by":"publisher","unstructured":"Shen, Z., Xu, H., Luo, T., Song, Y., He, Z.: UDAformer: Underwater image enhancement based on dual attention transformer. Computers Graphics. 111, 77\u201388 (2023). https:\/\/doi.org\/10.1016\/j.cag.2023.01.009","DOI":"10.1016\/j.cag.2023.01.009"},{"key":"2114_CR52","doi-asserted-by":"publisher","unstructured":"Khan, M.R.: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA: IEEE, Jan. 2024, pp. 1443\u20131452. (2024). https:\/\/doi.org\/10.1109\/WACV57701.2024.00148","DOI":"10.1109\/WACV57701.2024.00148"},{"key":"2114_CR53","doi-asserted-by":"publisher","unstructured":"Korhonen, J., You, J.: Peak signal-to-noise ratio revisited: Is simple beautiful? in 2012 Fourth International Workshop on Quality of Multimedia Experience, Melbourne, Australia: IEEE, pp. 37\u201338. (2012). https:\/\/doi.org\/10.1109\/QoMEX.2012.6263880","DOI":"10.1109\/QoMEX.2012.6263880"},{"key":"2114_CR54","doi-asserted-by":"publisher","unstructured":"Zhou Wang, A.C., Bovik, H.R., Sheikh, Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity, IEEE Trans. on Image Process., vol. 13, no. 4, pp. 600\u2013612. (2004). https:\/\/doi.org\/10.1109\/TIP.2003.819861","DOI":"10.1109\/TIP.2003.819861"},{"key":"2114_CR55","doi-asserted-by":"publisher","unstructured":"Panetta, K., Gao, C., Agaian, S.: Human-Visual-System-Inspired underwater image quality measures. IEEE J. Ocean. Eng. 41(3), 541\u2013551 (2016). https:\/\/doi.org\/10.1109\/JOE.2015.2469915","DOI":"10.1109\/JOE.2015.2469915"},{"key":"2114_CR56","doi-asserted-by":"publisher","unstructured":"Yang, M., Sowmya, A.: An Underwater Color Image Quality Evaluation Metric, IEEE Trans. on Image Process., vol. 24, no. 12, pp. 6062\u20136071. (2015). https:\/\/doi.org\/10.1109\/TIP.2015.2491020","DOI":"10.1109\/TIP.2015.2491020"},{"key":"2114_CR57","doi-asserted-by":"publisher","unstructured":"Mittal, A., Soundararajan, R., Bovik, A.C.: Making a \u2018Completely Blind\u2019 Image Quality Analyzer, IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209\u2013212. (2013). https:\/\/doi.org\/10.1109\/LSP.2012.2227726","DOI":"10.1109\/LSP.2012.2227726"},{"key":"2114_CR58","doi-asserted-by":"publisher","unstructured":"Guo, C., et al.: Underwater Ranker: Learn Which Is Better and How to Be Better,., arXiv. (2022). https:\/\/doi.org\/10.48550\/ARXIV.2208.06857","DOI":"10.48550\/ARXIV.2208.06857"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02114-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-02114-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02114-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T04:17:56Z","timestamp":1770783476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-02114-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":58,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2114"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-02114-8","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,15]]},"assertion":[{"value":"1 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"48"}}