Abstract
Underwater images often suffer from blurring and color distortion due to absorption and scattering in the water. Such effects are undesirable since they may hinder computer vision tasks. Many underwater image enhancement techniques have been explored to address this issue, each to varying degrees of success. The large variety of distortions in underwater images is difficult to handle by any singular method. This study observes four underwater image enhancement methods, i.e., Underwater Light Attenuation Prior (ULAP), statistical Background Light and Transmission Map estimation (BLTM), and Natural-based Underwater Image Color Enhancement (NUCE), and Global–Local Networks (GL-Net). These methods are evaluated on the Underwater Image Enhancement Benchmark (UIEB) dataset using quantitative metrics, e.g., SSIM, PSNR, and CIEDE2000 as the metrics. Additionally, a qualitative analysis of image quality attributes is also performed. The results show that GL-Net achieves the best enhancement result, but based on the qualitative assessment, this method still has room for improvement. A proper combination between the non-learning-based component and learning-based component should be investigated to further improve the robustness of the method.
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References
Han, M., Lyu, Z., Qiu, T., Xu, M.: A review on intelligence dehazing and color restoration for underwater images. IEEE Trans. Syst. Man Cybern, Syst. 50, 1820–1832 (2020). https://doi.org/10.1109/TSMC.2017.2788902
Lu, H., Li, Y., Serikawa, S.: Single underwater image descattering and color correction. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1623–1627. IEEE, South Brisbane, Queensland, Australia (2015). https://doi.org/10.1109/ICASSP.2015.7178245
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. on Broadcast. 66, 153–169 (2020). https://doi.org/10.1109/TBC.2019.2960942
Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., Tao, D.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. on Image Process. 29, 4376–4389 (2020). https://doi.org/10.1109/TIP.2019.2955241
Wu, S., Luo, T., Jiang, G., Yu, M., Xu, H., Zhu, Z., Song, Y.: A two-stage underwater enhancement network based on structure decomposition and characteristics of underwater imaging. IEEE J. Oceanic Eng. 46, 1213–1227 (2021). https://doi.org/10.1109/JOE.2021.3064093
Fu, X., Cao, X.: Underwater image enhancement with global–local networks and compressed-histogram equalization. Signal Processing: Image Communication. 86, 115892 (2020). https://doi.org/10.1016/j.image.2020.115892
Mohd Azmi, K.Z., Abdul Ghani, A.S., Md Yusof, Z., Ibrahim, Z.: Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm. Applied Soft Computing. 85, 105810 (2019). https://doi.org/10.1016/j.asoc.2019.105810
Iqbal, K., Odetayo, M., James, A., Salam, R.A., Hj Talib, A.Z.: Enhancing the low quality images using Unsupervised Colour Correction Method. In: 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 1703–1709. IEEE, Istanbul, Turkey (2010). https://doi.org/10.1109/ICSMC.2010.5642311
Li, C., Tang, S., Kwan, H.K., Yan, J., Zhou, T.: Color correction based on CFA and enhancement based on retinex with dense pixels for underwater images. IEEE Access. 8, 155732–155741 (2020). https://doi.org/10.1109/ACCESS.2020.3019354
McCann, J.J., Frankle, J.A.: Method and apparatus for lightness imaging. U.S. Patent 4384336A. 53 (1983)
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: Hong, R., Cheng, W.-H., Yamasaki, T., Wang, M., Ngo, C.W. (eds.) Advances in Multimedia Information Processing – PCM 2018, pp. 678–688. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-030-00776-8_62
Lin, R., Liu, J., Liu, R., Fan, X.: Global structure-guided learning framework for underwater image enhancement. Vis Comput. (2021). https://doi.org/10.1007/s00371-021-02305-0
Yang, M., Hu, K., Du, Y., Wei, Z., Sheng, Z., Hu, J.: Underwater image enhancement based on conditional generative adversarial network. Signal Processing: Image Communication. 81, 115723 (2020). https://doi.org/10.1016/j.image.2019.115723
Islam, M.J., Xia, Y., Sattar, J.: Fast underwater image enhancement for improved visual perception. IEEE Robot. Autom. Lett. 5, 3227–3234 (2020). https://doi.org/10.1109/LRA.2020.2974710
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. on Image Process. 13, 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861
Luo, M.R., Cui, G., Rigg, B.: The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Res. Appl. 26, 340–350 (2001). https://doi.org/10.1002/col.1049
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Marvi, J.N., Rahadianti, L. (2023). Towards Robust Underwater Image Enhancement. In: Yusoff, M., Hai, T., Kassim, M., Mohamed, A., Kita, E. (eds) Soft Computing in Data Science. SCDS 2023. Communications in Computer and Information Science, vol 1771. Springer, Singapore. https://doi.org/10.1007/978-981-99-0405-1_15
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