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Adaptive Channel Attention-Based Deformable Generative Adversarial Network for Underwater Image Enhancement

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Sensor Systems and Software (S-Cube 2022)

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Abstract

In this paper, to effectively strengthen quality of underwater image enhancement from both channel and spatial viewpoints, an adaptive channel attention-based deformable generative adversarial networks (ACADGAN) framework is established. Main contributions are as follows. 1) By virtue of multi-branch convolution architecture with dilated convolution mechanism, the adaptive channel attention (ACA) is devised, such that channel weight can be adaptively recalibrated, and thereby significantly contributing to preserving content features from channel viewpoint. 2) By augmenting offset position of sampling point with respect to convolution kernel, the deformable convolution network (DCN) is created, such that detailed information of underwater image can be dramatically retained from spatial aspect. 3) The ACADGAN scheme is eventually proposed by integrating ACA and DCN modules with a deep generative adversarial network. Comprehensive experiments demonstrate the remarkable effectiveness and superiority of the developed ACADGAN scheme.

This work is supported by the National Natural Science Foundation of China (Grant 52271306), Innovative Research Foundation of Ship General Performance (Grant 31422120), and the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University (Grant 2022YBPY004).

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References

  1. Akkaynak, D., Treibitz, T.: A revised underwater image formation model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 6723–6732 (2018)

    Google Scholar 

  2. Alipour-Fard, T., Paoletti, M., Haut, J.M., Arefi, H., Plaza, J., Plaza, A.: Multibranch selective kernel networks for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 18(6), 1089–1093 (2020)

    Article  Google Scholar 

  3. Ancuti, C.O., Ancuti, C., Bekaert, P.: Effective single image dehazing by fusion. In: International Conference on Image Processing, Hong Kong, China, pp. 3541–3544 (2010)

    Google Scholar 

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

    Google Scholar 

  5. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the International Conference on Machine Learning, Sydney, Australia, pp. 214–223 (2017)

    Google Scholar 

  6. Berman, D., Treibitz, T., Avidan, S.: Diving into haze-lines: color restoration of underwater images. In: Proceedings of the British Machine Vision Conference, London, UK, vol. 1, pp. 1–12 (2017)

    Google Scholar 

  7. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  8. Chen, T., Wang, N., Wang, R., Zhao, H., Zhang, G.: One-stage CNN detector-based benthonic organisms detection with limited training dataset. Neural Netw. 144, 247–259 (2021)

    Article  Google Scholar 

  9. Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756–1769 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chiang, J.Y., Chen, Y.C., Chen, Y.F.: Underwater image enhancement: using wavelength compensation and image dehazing (WCID). In: International Conference on Advanced Concepts for Intelligent Vision Systems, Ghent, Belgium, pp. 372–383 (2011)

    Google Scholar 

  11. Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 764–773 (2017)

    Google Scholar 

  12. 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, Sydney, Australia, pp. 825–830 (2013)

    Google Scholar 

  13. Ebner, D.H.: Color constancy. Vis. Res. 51(7), 674–700 (2011)

    Article  MATH  Google Scholar 

  14. Fabbri, C., Islam, M.J., Sattar, J.: Enhancing underwater imagery using generative adversarial networks. In: International Conference on Robotics and Automation, Brisbane, QLD, Australia, pp. 7159–7165 (2018)

    Google Scholar 

  15. Feifei, S., Xuemeng, Z., Guoyu, W.: An approach for underwater image denoising via wavelet decomposition and high-pass filter. In: International Conference on Intelligent Computation Technology and Automation, Shenzhen, China, vol. 2, pp. 417–420 (2011)

    Google Scholar 

  16. Fu, X., Fan, Z., Ling, M., Huang, Y., Ding, X.: Two-step approach for single underwater image enhancement. In: International Symposium on Intelligent Signal Processing and Communication Systems, Xiamen, China, pp. 789–794 (2017)

    Google Scholar 

  17. Gao, W., Zhang, L., Huang, W., Min, F., He, J., Song, A.: Deep neural networks for sensor-based human activity recognition using selective kernel convolution. IEEE Trans. Instrum. Meas. 70, 1–13 (2021)

    Google Scholar 

  18. Harris, C., Stephens, M., et al.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, vol. 15, pp. 10–5244. Citeseer (1988)

    Google Scholar 

  19. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Miami Beach, FL, USA, pp. 1956–1963 (2009)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. Jaffe, J.S.: Computer modeling and the design of optimal underwater imaging systems. IEEE J. Oceanic Eng. 15(2), 101–111 (1990)

    Article  Google Scholar 

  22. Jia, D., Ge, Y.: Underwater image de-noising algorithm based on nonsubsampled contourlet transform and total variation. In: International Conference on Computer Science and Information Processing, Xi’an, China, pp. 76–80 (2012)

    Google Scholar 

  23. Jian, S., Wen, W.: Study on underwater image denoising algorithm based on wavelet transform. In: International Conference on Control Engineering and Artificial Intelligence, Kuala Lumpur, Malaysia, vol. 806, p. 012006 (2017)

    Google Scholar 

  24. Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., Song, M.: Neural style transfer: a review. IEEE Trans. Vis. Comput. Graph. 26(11), 3365–3385 (2019)

    Article  Google Scholar 

  25. Lee, S., Yun, S., Nam, J.H., Won, C.S., Jung, S.W.: A review on dark channel prior based image dehazing algorithms. EURASIP J. Image Video Process. 2016(1), 4–26 (2016)

    Article  Google Scholar 

  26. Li, C., Anwar, S., Porikli, F.: Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn. 98, 107038 (2020)

    Article  Google Scholar 

  27. Li, C., Guo, J., Guo, C.: Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal. Proc. Lett. 25(3), 323–327 (2018)

    Article  Google Scholar 

  28. Li, J., Skinner, K.A., Eustice, R.M., Johnson-Roberson, M.: WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot. Autom. Lett. 3(1), 387–394 (2017)

    Google Scholar 

  29. Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 510–519 (2019)

    Google Scholar 

  30. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  31. Lu, H., Li, Y., Zhang, L., Serikawa, S.: Contrast enhancement for images in turbid water. J. Opt. Soc. Am. A-Opt. Image Sci. 32(5), 886–893 (2015)

    Article  Google Scholar 

  32. Ludvigsen, M., Sortland, B., Johnsen, G., Singh, H.: Applications of geo-referenced underwater photo mosaics in marine biology and archaeology. Oceanography 20(4), 140–149 (2007)

    Article  Google Scholar 

  33. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 2794–2802 (2017)

    Google Scholar 

  34. McGlamery, B.: A computer model for underwater camera systems. In: Ocean Optics, Monterey, CA, USA, vol. 208, pp. 221–231 (1980)

    Google Scholar 

  35. Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014). https://arxiv.org/abs/1411.1784

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

    Article  Google Scholar 

  37. Priyadharsini, R., Sharmila, T.S., Rajendran, V.: A wavelet transform based contrast enhancement method for underwater acoustic images. Multidimension. Syst. Signal Process. 29(4), 1845–1859 (2018)

    Article  Google Scholar 

  38. Singh, R., Biswas, M.: Adaptive histogram equalization based fusion technique for hazy underwater image enhancement. In: IEEE International Conference on Computational Intelligence and Computing Research, Chennai, India, pp. 1–5 (2016)

    Google Scholar 

  39. Wang, N., Er, M.J.: Self-constructing adaptive robust fuzzy neural tracking control of surface vehicles with uncertainties and unknown disturbances. IEEE Trans. Control Syst. Technol. 23(3), 991–1002 (2014)

    Article  Google Scholar 

  40. Wang, N., Er, M.J.: Direct adaptive fuzzy tracking control of marine vehicles with fully unknown parametric dynamics and uncertainties. IEEE Trans. Control Syst. Technol. 24(5), 1845–1852 (2016)

    Article  Google Scholar 

  41. Wang, N., Er, M.J., Sun, J.C., Liu, Y.C.: Adaptive robust online constructive fuzzy control of a complex surface vehicle system. IEEE Trans. Cybern. 46(7), 1511–1523 (2015)

    Article  Google Scholar 

  42. Wang, N., Karimi, H.R., Li, H., Su, S.F.: Accurate trajectory tracking of disturbed surface vehicles: a finite-time control approach. IEEE/ASME Trans. Mechatron. 24(3), 1064–1074 (2019)

    Article  Google Scholar 

  43. Wang, N., Qian, C., Sun, J.C., Liu, Y.C.: Adaptive robust finite-time trajectory tracking control of fully actuated marine surface vehicles. IEEE Trans. Control Syst. Technol. 24(4), 1454–1462 (2015)

    Article  Google Scholar 

  44. Wang, N., Wang, Y., Er, M.J.: Review on deep learning techniques for marine object recognition: architectures and algorithms. Control. Eng. Pract. 118, 104458 (2022)

    Article  Google Scholar 

  45. Wang, W., Wu, X., Yuan, X., Gao, Z.: An experiment-based review of low-light image enhancement methods. IEEE Access 8, 87884–87917 (2020)

    Article  Google Scholar 

  46. Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: video restoration with enhanced deformable convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA, pp. 1954–1963 (2019)

    Google Scholar 

  47. Wang, Z., Chen, J., Hoi, S.C.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3365–3387 (2020)

    Article  Google Scholar 

  48. Whitcomb, L., Yoerger, D.R., Singh, H., Howland, J.: Advances in underwater robot vehicles for deep ocean exploration: navigation, control, and survey operations. In: Hollerbach, J.M., Koditschek, D.E. (eds.) Robotics Research, pp. 439–448. Springer, London (2000). https://doi.org/10.1007/978-1-4471-0765-1_53

    Chapter  Google Scholar 

  49. Yang, H., Chen, P., Huang, C., Zhuang, Y., Shiau, Y.: Low complexity underwater image enhancement based on dark channel prior. In: International Conference on Innovations in Bio-inspired Computing and Applications, Shenzhen, China, pp. 17–20 (2011)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  52. Zhao, J., Mathieu, M., LeCun, Y.: Energy-based generative adversarial network (2016). https://arxiv.org/abs/1609.03126

  53. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 2223–2232 (2017)

    Google Scholar 

  54. Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems, pp. 474–485 (1994). https://doi.org/10.1016/B978-0-12-336156-1.50061-6

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Authors and Affiliations

  1. School of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China

    Tingkai Chen & Xiangjun Kong

  2. School of Marine Engineering, Dalian Maritime University, Dalian, 116026, China

    Ning Wang & Yanzheng Chen

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  1. Tingkai Chen
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  2. Ning Wang
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  3. Xiangjun Kong
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  4. Yanzheng Chen
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Correspondence to Ning Wang .

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Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Hamid Reza Karimi 

  2. Dalian Maritime University, Dalian, China

    Ning Wang

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Chen, T., Wang, N., Kong, X., Chen, Y. (2023). Adaptive Channel Attention-Based Deformable Generative Adversarial Network for Underwater Image Enhancement. In: Karimi , H.R., Wang, N. (eds) Sensor Systems and Software. S-Cube 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 487. Springer, Cham. https://doi.org/10.1007/978-3-031-34899-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-34899-0_2

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