Visual enhancement of underwater images using Empirical Mode Decomposition
Visual enhancement of underwater images using Empirical Mode Decomposition
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DOI：
10.1016/j.eswa.2011.07.077
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发表时间：
2012-01-01
影响因子：
8.5
通讯作者：
Erturk, Sarp
中科院分区：
文献类型：
--
作者：
Celebi, Aysun Tasyapi;Erturk, Sarp
如今，大多数水下航行器都配备了视觉传感器。然而，由于现实生活中的照明条件，使用光学相机捕获的水下图像很可能具有较差的视觉质量。在这种情况下，应用图像增强方法来提高图像的视觉质量以及增强可解释性和可见性是有用的。为此，本文提出了一种基于经验模式分解（EMD）的水下图像增强算法。在所提出的方法中，最初使用 EMD 将水下图像的每个光谱分量分解为本征模态函数 (IMF)。然后通过组合不同权重的光谱通道的IMF来构造增强图像，以获得视觉质量提高的增强图像。权重估计过程是使用遗传算法自动执行的，遗传算法计算 IMF 的权重，从而优化重建图像的熵和平均梯度之和。结果表明，与对比度拉伸和直方图均衡等传统方法相比，所提出的方法提供了更好的结果。 (C) 2011 Elsevier Ltd. 保留所有权利。
Most underwater vehicles are nowadays equipped with vision sensors. However, it is very likely that underwater images captured using optic cameras have poor visual quality due to lighting conditions in real-life applications. In such cases it is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. In this paper, an Empirical Mode Decomposition (EMD) based underwater image enhancement algorithm is presented for this purpose. In the proposed approach, initially each spectral component of an underwater image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image. It is shown that the proposed approach provides superior results compared to conventional methods such as contrast stretching and histogram equalizing. (C) 2011 Elsevier Ltd. All rights reserved.