Underwater image enhancement through depth estimation based on random forest

Shen Chuan Tai, Ting Chou Tsai, Jyun Han Huang

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)


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.

期刊Journal of Electronic Imaging
出版狀態Published - 2017 11月 1

All Science Journal Classification (ASJC) codes

  • 原子與分子物理與光學
  • 電腦科學應用
  • 電氣與電子工程


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