TY - JOUR
T1 - Underwater image enhancement through depth estimation based on random forest
AU - Tai, Shen Chuan
AU - Tsai, Ting Chou
AU - Huang, Jyun Han
PY - 2017/11/1
Y1 - 2017/11/1
N2 - 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.
AB - 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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U2 - 10.1117/1.JEI.26.6.063026
DO - 10.1117/1.JEI.26.6.063026
M3 - Article
AN - SCOPUS:85042436753
VL - 26
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
SN - 1017-9909
IS - 6
M1 - 063026
ER -