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Underwater Image Denoising Based on Non-Local Methods

机译:基于非局部方法的水下图像降噪

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Due to the medium scattering and light absorption, the acquired underwater optical images suffer from poor visibility. Absorption and scattering effects are caused not only by the water itself but also by other floating particles. Moreover, backscattering is strengthened during the light is reflected and deflected multiple times by the suspended particles before reaching the camera. The backscattering gives rise to specific strong correlated noise together with a constant level degrading the image contrast. Although there are many effective dehazing methods to remove the veil background, they have ignored the influence of backscattering noise, which still exists in the dehazed images. In addition, because of the special characteristics of backscattering noise, the traditional image denoising methods are difficult to make sense, even cause distortion. Accordingly, this paper employed the non-local denoising methods which are referred to Non-Local Means algorithm and Block-Matching 3D collaborative filtering to suppress the backscattering noise. The experimental results show that they can effectively remove the backscattering noise of underwater image and achieve better image quality.
机译:由于介质的散射和光吸收,所获取的水下光学图像的可见性差。吸收和散射效应不仅由水本身引起,而且还由其他漂浮颗粒引起。此外,在到达相机之前,悬浮粒子会多次反射和偏转光,从而增强了反向散射。反向散射会产生特定的强相关噪声,并且恒定的电平会降低图像对比度。尽管有许多有效的除雾方法可以去除面纱背景,但是它们却忽略了在散射图像中仍然存在的反向散射噪声的影响。另外,由于背向散射噪声的特殊特性,传统的图像去噪方法难以理解,甚至会引起失真。因此,本文采用了非局部去噪方法,即非局部均值算法和块匹配3D协同滤波,以抑制反向散射噪声。实验结果表明,它们可以有效地消除水下图像的反向散射噪声并获得更好的图像质量。

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