基于经验模态分解的水下图像视觉增强

A. Çelebi, S. Ertürk
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引用次数: 13

摘要

现在大多数水下航行器都配备了视觉传感器。然而,由于水下的照明条件,使用光学相机拍摄的水下图像质量可能很差。在这种情况下,有必要对水下图像应用图像增强方法,以提高视觉质量和可解释性。为此,本文将基于经验模态分解(EMD)的图像增强算法应用于水下图像。在文献中,EMD已被证明特别适用于非线性和非平稳信号,因此在现实生活中提供了非常有用的应用。在本文的方法中,首先使用EMD将彩色水下图像的R、G和B通道分别分解为内禀模态函数(IMFs)。然后,将各通道的权重不同的imf组合,构造增强图像,得到视觉质量提高的新图像。结果表明,与传统的图像增强方法(如对比度拉伸)相比,该方法具有更好的增强效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Empirical mode decomposition based visual enhancement of underwater images
Most underwater vehicles are nowadays equipped with vision sensors. However, underwater images captured using optic cameras can be of poor quality due to lighting conditions underwater. In such cases it is necessary to apply image enhancement methods to underwater images in order to enhance visual quality as well as interpretability. In this paper, an Empirical Mode Decomposition (EMD) based image enhancement algorithm is applied to underwater images for this purpose. EMD has been shown to be particularly suitable for non-linear and non-stationary signals in the literature, and therefore provides very useful in real life applications. In the approach presented in this paper, initially each R, G and B channel of the color underwater image is separately decomposed into Intrinsic Mode Functions (IMFs) using EMD. Then, the enhanced image is constructed by combining the IMFs of each channel with different weights, so as to obtain a new image with increased visual quality. It is shown that the proposed approach provides superior results compared to conventional image enhancement methods such as contrast stretching.
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