基于白平衡EMD的水下图像视觉增强

S. Mallik, Salman Siddique Khan, U. C. Pati
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引用次数: 8

摘要

本文提出了一种基于经验模态分解(EMD)的白平衡输入增强水下图像视觉质量的算法。由于光照条件差,水下图像一般会出现模糊、散射等抖动现象。EMD是一种特别适用于非平稳和非线性信号的信号分解算法。首先,通过灰度世界技术对图像进行处理,灰度世界技术是一种白平衡方法,可以增强图像的对比度,去除图像中不需要的偏色。然后,将合成图像的每个R、G和B通道分解为其固有模态函数(IMFs)。通过将每个通道的imf与不同的优化权重相结合,构建最终的增强图像。在白平衡处理后的图像上实现EMD算法以恢复图像的颜色。我们的实验结果通过降低图像中的噪声和伪影来增强图像的对比度。为了展示定量增强的效果,计算了灰度共生矩阵(GLCM)、峰值信噪比(PSNR)和均方误差(MSE),并对不同的常规增强方法进行了比较。与传统方法相比,所提出的方法可以获得更好的增强图像,并提高视觉质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual enhancement of underwater image by white-balanced EMD
In this paper, an algorithm has been proposed based on Empirical Mode Decomposition (EMD) with a white balanced input to enhance the visual quality of the under-water images. Generally, the underwater images are whisked by blurring effect, scattering effect etc. because of poor lighting condition. EMD is a signal decompose algorithm which is particularly useful for non-stationary and non-linear signals. First of all, the image is processed through the Gray World technique which is a white balance approach to enhance the contrast of the image and to remove the unwanted color cast in the image. Then, each R, G and B channel of the resultant image is decomposed into its Intrinsic Mode Functions(IMFs). Final enhanced image has been constructed by combining the IMFs of each channel with different optimised weights. The EMD algorithm is implemented on the resultant image of White balanced process to restore the color. Our experimental results enhance the contrast of the image by reducing noise as well as artifacts in the image. To show the quantitative enhanced result, Gray Level Cooccurrence Matrix(GLCM), Peak Signal to Noise Ratio(PSNR) and Mean Square Error(MSE) are calculated and compared with different conventional enhancement methods. The proposed method results in superior enhanced image with increased visual quality compared to conventional methods.
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