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Efficient underwater image restoration utilizing modified dark channel prior

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Abstract

An effective algorithm called Modified Underwater Dark Channel Prior (MUWDCP) based on the image prior for haze removal in underwater images is proposed. The proposed algorithm computes underwater dark channel information using only blue-green channels exploiting the fact that red color due to low frequency undergoes high absorption and drops at about a depth of 3 m. To address the issue of atmospheric light estimation and enhance its robustness, we propose a novel global atmospheric light estimation method based on arithmetic Mode operation. MUWDCP does not rely only upon the bright pixels of the underwater dark channel for atmospheric light estimation instead, it computes global atmospheric light from the degraded image itself. Unlike most underwater image restoration methods, which estimate transmission maps of only one channel, MUWDCP estimates transmission of all three-color channels while maintaining less computational complexity. MUWDCP has been found to provide significantly improved visual quality of the restored underwater image. The proposed method has been evaluated using subjective and objective quality metrics like Entropy, Natural Image Quality Evaluator (NIQE), Underwater Color Image Quality Evaluation (UCIQE), and Underwater Image Quality Metrics (UIQM). The experimentation shows that MUWDCP performs better as compared to various state-of-the-art algorithms. The proposed algorithm proffers Entropy, NIQE, and UIQM scores of about 7.18, 2.88, and 4.57 respectively, which are better than the state-of-the-art. Thus, the results demonstrate the effectiveness of the proposed scheme.

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Data availability

The datasets used for testing the developed algorithm have been taken from following digital object identifiers: doi: 10.1109/OCEANSAP.2016.7485524, doi: 10.1109/ICCVW.2013.113, doi: https://doi.org/10.1109/MCG.2016.26

References

  1. Abril L, Méndez T, Dudek G (2005) Color correction of underwater images for aquatic robot inspection. In: Rangarajan A, Vemuri BC, Yuille AL (eds) Lecture Notes in Computer Science, vol 3757. Springer, pp 60–73

    Google Scholar 

  2. Ahmad M, Khan AM, Hussain R et al (2016) Unsupervised geometrical feature learning from hyperspectral data. IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece, pp. 1–6

  3. Amanda D, Felipe C, Joel G, Silvia B (2016) A dataset to evaluate underwater image restoration methods. IEEE OCEANS 2016-Shanghai, pp. 1–6, https://doi.org/10.1109/OCEANSAP.2016.7485524

  4. Carlevaris-Bianco N, Mohan A, Eustice RM (2010) Initial results in underwater single image dehazing. Proc MTS/IEEE Seattle Oceans 27(3):1–8

    Google Scholar 

  5. Chao L, Wang M (2010) Removal of water scattering. Proc 2nd Int Conf Comput Eng Technol 2:V2-35–V2-39. https://doi.org/10.1109/ICCET.2010.5485339

    Article  Google Scholar 

  6. Chiang J, Chen Y (2012) Underwater image enhancement by wavelength compensation and dehazing. IEEE TIP 21(4):1756–1769

    MathSciNet  MATH  Google Scholar 

  7. Drews P, Nascimento E, Moraes F et al (2013) Transmission estimation in underwater single images. In: Proc. IEEE Int. Conf. Comput. Vis. Workshops, Sydney, NSW, Australia, pp. 825–830

  8. Drews P, Nascimento ER, Botelho S, Campos M (2016) Underwater depth estimation and image restoration based on single images. IEEE Comput Graph Appl 36(2):24–35

    Article  Google Scholar 

  9. Duntley SQ, Boileau AR, Preisendorfer RW (1957) Image transmission by the troposphere I. J Opt Soc Am 47(6):499–506

    Article  Google Scholar 

  10. Fattal R (2008) Single image dehazing. ACM TOG 27(3):1–9. https://doi.org/10.1145/1360612.1360671

    Article  Google Scholar 

  11. Fayaz S, Parah SA, Qureshi GJ, Kumar V (2021) Underwater image restoration: a state-of-the-art-review. IET Image Process 15(2):269–285

    Article  Google Scholar 

  12. Galdran A (2018) Image dehazing by artificial multiple-exposure image fusion. Signal Process 149:135–147. https://doi.org/10.1016/j.sigpro.2018.03.008

    Article  Google Scholar 

  13. Gao Y, Li H, Wen S (2016) Restoration and enhancement of underwater images based on Bright Channel prior. Math Probl Eng 2016:15–15. https://doi.org/10.1155/2016/3141478

    Article  Google Scholar 

  14. Gao Y, Wang J, Li H, Feng L (2019) Underwater image enhancement and restoration based on local fusion. J Electron Imaging 28(4). https://doi.org/10.1117/1.JEI.28.4.043014

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

    Article  Google Scholar 

  16. He K, Sun J, Tang X (2009) Single image haze removal using dark channel prior. In: IEEE CVPR, pages 1956–1963

  17. He K, Sun J, Tang X (2009) Single image haze removal using Dark Channel prior. Proc. IEEE Conf. Computer Vision and Pattern Recognition

  18. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE TPAMI, Hong Kong, China 33(12):2341–2353

    Article  Google Scholar 

  19. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 30(12):2341–2353

    Google Scholar 

  20. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  21. Hou G, Li J, Wang G, Pan Z, Zhao X (2020) Underwater image dehazing and denoising via curvature variation regularization. Multimed Tools Appl 79:20199–20219. https://doi.org/10.1007/s11042-020-08759-z

    Article  Google Scholar 

  22. Huang SC, Chen BH, Cheng YJ (2014) An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems. IEEE Trans Intell Transp Syst 15(5):2321–2332

    Article  Google Scholar 

  23. Huang D, Wang Y, Song W, Sequeira J, Mavromatis S (2018) Shallow water image enhancement using relative global histogram stretching based on adaptive parameter acquisition. Multi Media Modeling 10704:453–465. https://doi.org/10.1007/978-3-319-73603-7_37

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Jiang H, Lu N (2018) Multi-scale residual convolutional neural network for haze removal of remote sensing images. Remote Sens 10(6):945

    Article  Google Scholar 

  26. Levin A, Lischinski D, Weiss Y (2006) A closed form solution to natural image matting. Proc. IEEE Conf. Computer Vision and Pattern Recognition

  27. Li C, Guo J, Chen S et al (Sep. 2016) Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging. In: Proc. IEEE Int. Conf. Image Process. (ICIP), Qinghai, China, pp. 1993–1997

  28. Li C, Quo J, Pang Y, Chen S, Wang J (Sep. 2016) Single underwater image restoration by blue-green channels dehazing and red channel correction. In: Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), pp. 1731–173

  29. Li Y, Lu H, Li CK, K et al., “Non-uniform descattering and de-blurring of underwater images,” Mobile Netw Appl, vol. 23, no. 2, pp. 352–362, 2018

  30. Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2020) An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process 29:4376–4389. https://doi.org/10.1109/TIP.2019.2955241

    Article  MATH  Google Scholar 

  31. Lu H, Li Y, Serikawa S (2016) Computer vision for ocean observing. Artif Intell Comput Vis, pp. 1–16, https://doi.org/10.1007/978-3-319-46245-5_1

  32. McGlamery BL (1980) A computer model for underwater camera systems. Proc SPIE:221–232. https://doi.org/10.1117/12.958279

  33. Narasimhan SG, Nayar SK (2000) Chromatic framework for vision in bad weather. Proc IEEE Conf Computer Vis Pattern Recognit 1:598–605. https://doi.org/10.1109/CVPR.2000.855874

    Article  Google Scholar 

  34. Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vis 48(3):233–254

    Article  MATH  Google Scholar 

  35. Narasimhan S, Nayar S (2003) Contrast restoration of weather degraded images. IEEE TPAMI 25(6):713–724

    Article  Google Scholar 

  36. Pan P-W, Yuan F, Cheng E (2019) De-scattering and edge-enhancement algorithms for underwater image restoration. Front Inform Technol Electron Eng 20(6):862–871

    Article  Google Scholar 

  37. Panetta K, Gao C, Agaian S (2016) Human-visual-system-inspired underwater image quality measures. IEEE J Ocean Eng 41(3):541–551. https://doi.org/10.1109/JOE.2015.2469915

    Article  Google Scholar 

  38. Peng YT, Cosman PC (2017) Underwater image restoration based on image blurriness and light absorption. IEEE Trans Image Process 26(4):1579–1594

    Article  MathSciNet  MATH  Google Scholar 

  39. Peng YT, Zhao X, Cosman PC (2015) Single underwater image enhancement using depth estimation based on blurriness. IEEE International Conference on Image Processing (ICIP), Quebec City, pp 4952–4956. https://doi.org/10.1109/ICIP.2015.7351749

  40. Sandbhor B, Kharat GU (2015) A review on underwater image enhancement techniques. Int J Adv Res Comput Sci Softw Eng 5(5):676–680

  41. Schechner YY, Karpel N (2004) Clear underwater vision. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp I–I. https://doi.org/10.1109/CVPR.2004.1315078

  42. Schechner Y, Karpel N (2005) Recovery of underwater visibility and structure by polarization analysis. IEEE JOE 30(3):570–587

    Google Scholar 

  43. Schettini R, Corchs S (2010) Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J Adv Signal Process 2010:1–14. https://doi.org/10.1155/2010/746052

    Article  Google Scholar 

  44. Sequeira G, Mekkalki V, Prabhu J, Borkar S, Desai M (2021) Hybrid Approach for Underwater Image Restoration and Enhancement. 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 427–432, https://doi.org/10.1109/ESCI50559.2021.9397058

  45. Shen Y, Wang Y, Lv H, Qian J (2015) Removal of thin clouds in landsat-8 oli data with independent component analysis. Remote Sens 7(9):11481–11500

    Article  Google Scholar 

  46. Singh D, Kumar V (2017) Dehazing of remote sensing images using improved restoration model based dark channel prior. Imaging Sci J 65(5):282–292

    Article  Google Scholar 

  47. Song W, Wang Y, Huang D, Tjondronegoro D (2018) A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. Adv Multimed Inf Process 11164:678–688. https://doi.org/10.1007/978-3-030-00776-8_62

    Article  Google Scholar 

  48. Song Y, Nakath D, She M et al (2022) Optical imaging and image restoration techniques for deep ocean mapping: a comprehensive survey. PFG Springer 90:243–267. https://doi.org/10.1007/s41064-022-00206-y

    Article  Google Scholar 

  49. Sun L, Latifovic R, Pouliot D (2017) Haze removal based on a fully automated and improved haze optimized transformation for landsat imagery over land. Remote Sens 9(10):972

    Article  Google Scholar 

  50. Tan R (Jun. 2008) Visibility in bad weather from a single image. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, Alaska, USA, pages 1–8

  51. Wang W, Yuan X (2017) Recent advances in image dehazing. IEEE/ CAA J Autom Sin 4(3):410–436

    Article  MathSciNet  Google Scholar 

  52. Wang N, Zheng H, Zheng B (2017) Underwater image restoration via maximum attenuation identification. IEEE Access 5:18941–18952. https://doi.org/10.1109/ACCESS.2017.2753796

    Article  Google Scholar 

  53. Wang Y, Song W, Fortino G, Qi LZ, Zhang W, Liotta A (Jul. 2019) An experimental-based review of image enhancement and image restoration methods for underwater imaging. Special section on advanced optical imaging for extreme environments, Article in IEEE Access, pp (99):1–1, https://doi.org/10.1109/ACCESS.2019.2932130

  54. Yang HY, Chen PY, Huang CC et al (Dec. 2011) Low complexity underwater image enhancement based on dark channel prior. In: Proc. 2nd Int. Conf. Innov. Bio-Inspired Comput. Appl., Shenzhan, China, pp. 17–20

  55. Zhao X, Jin T, Qu S (2015) Deriving inherent optical properties from background color and underwater image enhancement. OceanEng. 94:163–172. https://doi.org/10.1016/j.oceaneng.2014.11.036

    Article  Google Scholar 

  56. Zhuang P, Ding X (2020) Underwater image enhancement using an edge-preserving filtering retinex algorithm. Multimed Tools Appl 79:17257–17277. https://doi.org/10.1007/s11042-019-08404-4

    Article  Google Scholar 

  57. Zomet A, Peleg S (2002) Multi-sensor super resolution. Proc. IEEE Workshop Applications of Computer Vision

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Authors and Affiliations

  1. Department of Electronics and IT, University of Kashmir, Hazratbal, Srinagar, India

    Sheezan Fayaz & Shabir A. Parah

  2. Higher Education Department, Government of J and K, Jammu, India

    G. J. Qureshi

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  1. Sheezan Fayaz
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  3. G. J. Qureshi
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Correspondence to Shabir A. Parah.

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Fayaz, S., Parah, S.A. & Qureshi, G.J. Efficient underwater image restoration utilizing modified dark channel prior. Multimed Tools Appl 82, 14731–14753 (2023). https://doi.org/10.1007/s11042-022-13828-6

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