Abstract
In this paper, we propose an algorithm for underwater light field image restoration and enhancement. The algorithm is based on the image haze removal using dark channel prior and Pyramid image fusion. In the process of restoration and enhancement, the light field image is converted into four-dimensional data, and each angle image of the four-dimensional data is restored and enhanced. After this, the four-dimensional data is refocused and all-focused again to get the clearer image. Final experimental result shows that the proposed algorithm has good effects on the restoration and enhancement of underwater light field image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McGlamery BL (1980) A computer model for underwater camera systems. Ocean optics VI. Int Soc Optics Photonics, pp 221–231
Arnold-Bos A, Malkasset JP, Kervern G (2005) Towards a model-free denoising of underwater optical images. In: Arnold-Bos A (ed) Oceans 2005-Europe. Brest, France: IEEE Computer Society, pp 527–532
Arnold-Bos A, Malkasset JP, Kervern G (2005) A preprocessing framework for automatic underwater images denoising. In: Arnold-Bos A (ed) Proceedings of the european conference on propagation and systems 2005. Brest, France: European Conference on Propagation and Systems, pp 15–18
Chao L, Wang M (2010) Removal of water scattering. In: Chao L (ed) Proceedings of the 2010 international conference on computer engineering and technology. Chengdu, China: IEEE computer society 445 Hoes Lane-P. O. Box 1331 Piscataway NJ 08855–1331 United States, pp 235–239
Zhang X, Li C (2014) Calibration and imaging model of light field camera with microlens array. Acta Optica Sinica 34(12):1211005
Gortler SJ, Grzeszczuk R, Szeliski R et al (1996) The lumigraph. In: Proceedings of the 23rd annual conference on computer graphics and interactive techniques, pp 43–54
Mcmillan L, Bishop G (1996) Plenoptic modeling: an image-based rendering system. In: Proceedings of the 22nd annual conference on computer graphics and interactive techniques, pp 43–54
Nava FP, Luke JP (2009) Simultaneous estimation of super-resolved depth and all-in-focus images from a plenoptic camera. The true vision-capture, transmission and display of 3D video, pp 1–4
Ng R, Levoy M, Bredif M et al (2005) Light field photography with a hand—held plenoptic camera. Computer science technical report
He K, Sun J, Tang X (2009) Single image haze removal using dark channel prior. In: IEEE CVPR
Ancuti CO, Ancuti C, Bekaert P (2010) Effective single image dehazing by fusion. IEEE
Ancuti C, Ancuti CO (2016) Multi-scale underwater descattering. ICPR, pp 4202–4207
Levoy M, Hanrahan P (1996) Light field rendering. In: ACM trans proceedings of the 23rd annual conference on computer graphics and interactive techniques, pp 31–42
Zhou C, Nayar SK (2011) Computational cameras: convergence of optics and processing. IEEE Trans Image Process 20(12):3322–3340
Pertuz S, Puig D, Garcia MA, Fusiello A (2013) Generation of all-in-focus images by noise-robust selective fusion of limited depth-of-field images. IEEE Trans Image Process 22(3):1242–1251
Tian J, Chen L (2012) Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure. Sig Process 92(9):2137–2146
Jhohura FT, Howlader T, Rahman SMM (2014) Bayesian fusion of ensemble of multifocused noisy images. Circuits Sys Signal Process 1–22
Pertuz S, Garcia MA, Puig D (2014) Efficient focus sampling through depth-of-field calibration. Int J Comput Vision 1–12
Teixeira R, Aizawa K (2014) Simultaneous acquisition of multiple images with higher dynamic range. In: 2014 IEEE International Conference on acoustics, speech and signal processing (ICASSP), pp 1355–1359. IEEE
Acknowledgements
This research was partially supported by the National Nature Science Foundation of China (Grant no. 51575332 and no. 61673252) and the key research project of Ministry of science and technology (Grant no. 2016YFC0302401).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cui, W., Li, C., Zhang, C., Zhang, X. (2018). Restoration and Enhancement of Underwater Light Field Image. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds) Advanced Manufacturing and Automation VII. IWAMA 2017. Lecture Notes in Electrical Engineering, vol 451. Springer, Singapore. https://doi.org/10.1007/978-981-10-5768-7_9
Download citation
DOI: https://doi.org/10.1007/978-981-10-5768-7_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5767-0
Online ISBN: 978-981-10-5768-7
eBook Packages: EngineeringEngineering (R0)