Wang, H., Dong, L., Song, W., Zhao, X., Xia, J., Liu, T.: Improved u-net-based novel segmentation algorithm for underwater mineral image. Intell Autom Soft Comput 32(3) (2022)

Wang, Y., Tang, C., Cai, M., Yin, J., Wang, S., Cheng, L., Wang, R., Tan, M.: Real-time underwater onboard vision sensing system for robotic gripping. IEEE Trans. Instrum. Meas. 70, 1–11 (2020)

Article  MATH  Google Scholar 

Ren, B., Feng, J., Wei, Y., Huang, Y.: Underwater target detection algorithm based on improved yolov5. Adv. Eng. Technol. Res. 1(3), 713–713 (2022)

Article  MATH  Google Scholar 

Liu, B., Liu, Z., Men, S., Li, Y., Ding, Z., He, J., Zhao, Z.: Underwater hyperspectral imaging technology and its applications for detecting and mapping the seafloor: a review. Sensors 20(17), 4962 (2020)

Article  ADS  MATH  Google Scholar 

Jung, Y.H., Kim, G., Yoo, W.S.: Study on distortion compensation of underwater archaeological images acquired through a fisheye lens and practical suggestions for underwater photography-a case of Taean mado shipwreck no. 1 and no. 2. J. Conserv. Sci. 37(4), 312–321 (2021)

Article  Google Scholar 

Horning, N., Robinson, J.A., Sterling, E.J., Turner, W.: Remote sensing for ecology and conservation: a handbook of techniques. Oxford University Press (2010)

Mobley, C.D.: Radiative transfer in the ocean. Encycl. Ocean Sci. 4, 2321–2330 (2001)

Article  MATH  Google Scholar 

He, D.M., Seet, G.G.: Divergent-beam lidar imaging in turbid water. Opt. Lasers Eng. 41(1), 217–231 (2004)

Article  MATH  Google Scholar 

Treibitz, T., Schechner, Y.Y.: Active polarization descattering. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 385–399 (2008)

Article  Google Scholar 

Drews, P., Nascimento, E., Moraes, F., Botelho, S., Campos, M.: Transmission estimation in underwater single images. In: Proceedings of the IEEE international conference on computer vision workshops, pp. 825–830 (2013)

Li, C., Quo, J., Pang, Y., Chen, S., Wang, J.: Single underwater image restoration by blue-green channels dehazing and red channel correction. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1731–1735. IEEE (2016)

Zhang, W., Pan, X., Xie, X., Li, L., Wang, Z., Han, C.: Color correction and adaptive contrast enhancement for underwater image enhancement. Comput. Electr. Eng. 91, 106981 (2021)

Article  MATH  Google Scholar 

Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., Garcia, R.: Locally adaptive color correction for underwater image dehazing and matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–9 (2017)

Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., Tao, D.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29, 4376–4389 (2019)

Article  ADS  MATH  Google Scholar 

Islam, M.J., Xia, Y., Sattar, J.: Fast underwater image enhancement for improved visual perception. IEEE Robot. Autom. Lett. 5(2), 3227–3234 (2020)

Article  MATH  Google Scholar 

Buchsbaum, G.: A spatial processor model for object colour perception. J. Franklin Inst. 310(1), 1–26 (1980)

Article  MATH  Google Scholar 

Ancuti, C., Ancuti, C.O., Haber, T., Bekaert, P.: Enhancing underwater images and videos by fusion. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 81–88. IEEE (2012)

Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27(1), 379–393 (2017)

Article  ADS  MathSciNet  MATH  Google Scholar 

Song, H., Wang, R.: Underwater image enhancement based on multi-scale fusion and global stretching of dual-model. Mathematics 9(6), 595 (2021)

Article  MATH  Google Scholar 

Tao, Y., Dong, L., Xu, W.: A novel two-step strategy based on white-balancing and fusion for underwater image enhancement. IEEE Access 8, 217651–217670 (2020)

Article  MATH  Google Scholar 

Fu, X., Zhuang, P., Huang, Y., Liao, Y., Zhang, X.P., Ding, X.: A retinex-based enhancing approach for single underwater image. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4572–4576. IEEE (2014)

Zhang, S., Wang, T., Dong, J., Yu, H.: Underwater image enhancement via extended multi-scale retinex. Neurocomputing 245, 1–9 (2017)

Article  MATH  Google Scholar 

Zhuang, P., Wu, J., Porikli, F., Li, C.: Underwater image enhancement with hyper-laplacian reflectance priors. IEEE Trans. Image Process. 31, 5442–5455 (2022)

Article  ADS  MATH  Google Scholar 

Hitam, M.S., Awalludin, E.A., Yussof, W.N.J.H.W., Bachok, Z.: Mixture contrast limited adaptive histogram equalization for underwater image enhancement. In: 2013 International Conference on Computer Applications Technology (ICCAT), pp. 1–5. IEEE (2013)

Ghani, A.S.A., Isa, N.A.M.: Enhancement of low quality underwater image through integrated global and local contrast correction. Appl. Soft Comput. 37, 332–344 (2015)

Article  MATH  Google Scholar 

Zhang, W., Zhuang, P., Sun, H.H., Li, G., Kwong, S., Li, C.: Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement. IEEE Trans. Image Process. 31, 3997–4010 (2022)

Article  ADS  MATH  Google Scholar 

Zhang, W., Jin, S., Zhuang, P., Liang, Z., Li, C.: Underwater image enhancement via piecewise color correction and dual prior optimized contrast enhancement. IEEE Signal Process. Lett. 30, 229–233 (2023)

Article  ADS  MATH  Google Scholar 

He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)

MATH  Google Scholar 

Peng, Y.T., Cao, K., Cosman, P.C.: Generalization of the dark channel prior for single image restoration. IEEE Trans. Image Process. 27(6), 2856–2868 (2018)

Article  ADS  MathSciNet  MATH  Google Scholar 

Drews, P.L., Nascimento, E.R., Botelho, S.S., Campos, M.F.M.: Underwater depth estimation and image restoration based on single images. IEEE Comput. Graphics Appl. 36(2), 24–35 (2016)

Article  MATH  Google Scholar 

Liu, H., Chau, L.P.: Underwater image restoration based on contrast enhancement. In: 2016 IEEE International Conference on Digital Signal Processing (DSP), pp. 584–588. IEEE (2016)

Song, W., Wang, Y., Huang, D., Liotta, A., Perra, C.: Enhancement of underwater images with statistical model of background light and optimization of transmission map. IEEE Trans. Broadcast. 66(1), 153–169 (2020)

Article  Google Scholar 

Akkaynak, D., Treibitz, T.: Sea-thru: A method for removing water from underwater images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1682–1691 (2019)

Liang, Z., Zhang, W., Ruan, R., Zhuang, P., Li, C.: Gifm: an image restoration method with generalized image formation model for poor visible conditions. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022)

Google Scholar 

Cheng, D., Prasad, D.K., Brown, M.S.: Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution. JOSA A 31(5), 1049–1058 (2014)

Article  ADS  MATH  Google Scholar 

Land, E.H.: The retinex theory of color vision. Sci. Am. 237(6), 108–129 (1977)

Article  MATH  Google Scholar 

Zhu, Z., Wei, H., Hu, G., Li, Y., Qi, G., Mazur, N.: A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. IEEE Trans. Instrum. Meas. 70, 1–23 (2020)

Article  MATH  Google Scholar 

Burt, P.J., Adelson, E.H.: The laplacian pyramid as a compact image code. In: Readings in computer vision, pp. 671–679. Elsevier (1987)

Hou, G., Zhao, X., Pan, Z., Yang, H., Tan, L., Li, J.: Benchmarking underwater image enhancement and restoration, and beyond. IEEE Access 8, 122078–122091 (2020)

Article  Google Scholar 

Carlevaris-Bianco, N., Mohan, A., Eustice, R.M.: Initial results in underwater single image dehazing. In: Oceans 2010 Mts/IEEE Seattle, pp. 1–8. IEEE (2010)

Song, W., Wang, Y., Huang, D., Tjondronegoro, D.: A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. In: Advances in Multimedia Information Processing–PCM 2018: 19th Pacific-Rim Conference on Multimedia, Hefei, China, September 21-22, 2018, Proceedings, Part I 19, pp. 678–688. Springer (2018)

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

Article  ADS  MATH  Google Scholar 

Wang, S., Ma, K., Yeganeh, H., Wang, Z., Lin, W.: A patch-structure representation method for quality assessment of contrast changed images. IEEE Signal Process. Lett. 22(12), 2387–2390 (2015)

Article  ADS  MATH  Google Scholar 

Yang, M., Sowmya, A.: An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24(12), 6062–6071 (2015)

Article  ADS  MathSciNet  MATH  Google Scholar 

Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Oceanic Eng. 41(3), 541–551 (2015)

Article  ADS  MATH  Google Scholar 

Guo, C., Wu, R., Jin, X., Han, L., Chai, Z., Zhang, W., Li, C.: Underwater ranker: Learn which is better and how to be better. In: Proceedings of the AAAI Conference on Artificial Intelligence (2023)

Li, C., Anwar, S., Hou, J., Cong, R., Guo, C., Ren, W.: Underwater image enhancement via medium transmission-guided multi-color space embedding. IEEE Trans. Image Process. 30, 4985–5000 (2021)

Article  ADS  Google Scholar 

Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)