Underwater image enhancement through depth estimation based on random forest

Shen-Chuan Tai; Ting-Chou Tsai; Jyun-Han Huang

doi:10.1117/1.JEI.26.6.063026

15 December 2017 Underwater image enhancement through depth estimation based on random forest

Shen-Chuan Tai, Ting-Chou Tsai, Jyun-Han Huang

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 6, 063026 (December 2017). https://doi.org/10.1117/1.JEI.26.6.063026

Abstract

Light absorption and scattering in underwater environments can result in low-contrast images with a distinct color cast. This paper proposes a systematic framework for the enhancement of underwater images. Light transmission is estimated using the random forest algorithm. RGB values, luminance, color difference, blurriness, and the dark channel are treated as features in training and estimation. Transmission is calculated using an ensemble machine learning algorithm to deal with a variety of conditions encountered in underwater environments. A color compensation and contrast enhancement algorithm based on depth information was also developed with the aim of improving the visual quality of underwater images. Experimental results demonstrate that the proposed scheme outperforms existing methods with regard to subjective visual quality as well as objective measurements.

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Shen-Chuan Tai, Ting-Chou Tsai, and Jyun-Han Huang "Underwater image enhancement through depth estimation based on random forest," Journal of Electronic Imaging 26(6), 063026 (15 December 2017). https://doi.org/10.1117/1.JEI.26.6.063026

Received: 24 August 2017; Accepted: 28 November 2017; Published: 15 December 2017

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