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浑浊水中图像的对比度增强。

Contrast enhancement for images in turbid water.

作者信息

Lu Huimin, Li Yujie, Zhang Lifeng, Serikawa Seiichi

出版信息

J Opt Soc Am A Opt Image Sci Vis. 2015 May 1;32(5):886-93. doi: 10.1364/JOSAA.32.000886.

Abstract

Absorption, scattering, and color distortion are three major degradation factors in underwater optical imaging. Light rays are absorbed while passing through water, and absorption rates depend on the wavelength of the light. Scattering is caused by large suspended particles, which are always observed in an underwater environment. Color distortion occurs because the attenuation ratio is inversely proportional to the wavelength of light when light passes through a unit length in water. Consequently, underwater images are dark, low contrast, and dominated by a bluish tone. In this paper, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. In addition, we develop a robust color lines-based ambient light estimator and a locally adaptive filtering algorithm for enhancing underwater images in shallow oceans. Furthermore, we propose a spectral characteristic-based color correction algorithm to recover the distorted color. The enhanced images have a reasonable noise level after the illumination compensation in the dark regions, and demonstrate an improved global contrast by which the finest details and edges are enhanced significantly.

摘要

吸收、散射和颜色失真水下光学成像中的三个主要退化因素。光线在穿过水的过程中被吸收,吸收率取决于光的波长。散射是由大的悬浮颗粒引起的,在水下环境中总是能观察到这些颗粒。当光在水中传播一个单位长度时,由于衰减率与光的波长成反比,所以会发生颜色失真。因此,水下图像暗、对比度低且以蓝色调为主。在本文中,我们提出了一种新颖的水下成像模型,该模型可补偿沿传播路径的衰减差异。此外,我们开发了一种基于颜色线的鲁棒环境光估计器和一种局部自适应滤波算法,用于增强浅海的水下图像。此外,我们提出了一种基于光谱特征的颜色校正算法来恢复失真的颜色。在对暗区进行光照补偿后,增强后的图像具有合理的噪声水平,并显示出改善的全局对比度,通过该对比度可以显著增强最精细的细节和边缘。

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