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Auto-layering wavelet transfer to remove the circumstance effect and noise

机译:自动分层小波转移以消除环境效应和噪音

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The accuracy of visibility measurement from night light sources image is usually affected by the circumstance light and noise. This paper presents an auto-layering wavelet transfer method to remove the circumstance effect and noise simultaneously. Firstly, the light propagation through the fog at night condition is formulized, where the model and features of night image with circumstance effect and noise is given. Secondly, we propose to use multi-scale features of wavelet transfer to decompose the image to remove the circumstance effect and noise, where an auto-layering method is used based on the energy ratio of wavelet coefficients. Experiments show that our method is able to remove the circumstance effect and noise simultaneously and to adjust the decomposed layering number automatically. Our method is not only suitable for many wavelet functions, but also preserves the light sources as well as their glows in the digital images. The relative error of using db4 is 3.16%, and the relative error of using sym2 is 2.02%.
机译:从夜间光源图像的可视性测量的准确性通常受环境光和噪声的影响。本文提出了一种自动分层小波传输方法,可同时消除环境效应和噪声。首先,制定通过雾通过雾的光传播,其中给出了环境效应和噪声的夜间图像的模型和特征。其次,我们建议使用小波传输的多尺度特征来分解图像以消除基于小波系数的能量比使用自动分层方法的环境效应和噪声。实验表明,我们的方法能够同时去除环境效应和噪声,并自动调整分解的分层数。我们的方法不仅适用于许多小波函数,而且还保留了光源以及它们在数字图像中的发光。使用DB4的相对误差为3.16%,使用SYM2的相对误差为2.02%。

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