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Preservation of image edge feature based on snowfall model smoothing filter

机译:基于降雪模型平滑滤波器的图像边缘特征的保存

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Abstract This paper proposed a snowfall model as a novel smoothing filter. The pixel composition of the image was similar to the geographic features, so it could be smooth because of snow accumulation. In the snowfall processing, luminance changes are linked to terrain and snowfall amount. Curvature and luminance gradient decided the amount of snowfall; the amount of snowfall became large on the parts where the curvature was large, and it became little on the parts where the gradient was steep. Snowfall algorithm simulates the natural snowfall process, which nonlinear diffusion and the target feature could be preserved well. Snowfall model has the same function as the Gaussian filter. The number of regions was reduced after Gaussian filter and snowfall model smoothing, respectively. The contrast experiment was carried out based on Watershed algorithm. The image area segmentation that pretreated through snowfall model was compared with Gaussian filter smoothing. The experimental result showed that the proposed snowfall model was a smoothing filter. It was able to realize edge preservation, which was the original purpose, and it was also possible to apply to region segmentation.
机译:摘要本文提出了一种新颖的平滑过滤器的降雪模式。图像的像素组成类似于地理特征,因此由于积雪累积可能是光滑的。在降雪处理中,亮度变化与地形和降雪量相关联。曲率和亮度梯度决定降雪量;降雪量在曲率大的部分上变大,并且梯度陡峭的部件上变得很少。降雪算法模拟了天然降雪过程,可以良好地保留非线性扩散和目标特征。降雪模型具有与高斯过滤器相同的功能。高斯滤波器和降雪模型平滑后,区域的数量减少。对比实验是基于流域算法进行的。将通过降雪模型进行预处理的图像区域分割与高斯滤波器平滑进行了比较。实验结果表明,所提出的降雪模型是平滑过滤器。它能够实现边缘保存,这是原始目的,也可以应用于区域分割。

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