多尺度梯度向量场及其在图像去噪和增强中的应用

Jinye Peng, Wanhai Yang, Yichun Wang
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引用次数: 1

摘要

图像去噪与增强是图像处理中的重要任务之一。自蛇模型是基于偏微分方程(PDE)的一种最基本、最重要的图像去噪方法,其中停止函数非常重要,通常与图像的梯度有关。但是我们通常用单一的尺度来获取近似图像的梯度时,很难选择平滑尺度。因此,为了提高自蛇模型的性能,我们提出了一种多尺度梯度向量,该梯度向量是根据我们定义的一些标尺将图像从粗到细的不同尺度梯度向量集成在一起的。实验结果证明了该方法的有效性。
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Multiscale Gradient Vector Field with Application to Image Denoising and Enhancement
Image denoising and enhancement is one of the most important tasks in image processing. Self-snake model is a most basic and important image denoising method based on PDE (Partial Differential Equation), in which the stop function is very important and is usually related to the gradient of the image. But it¿s difficult to choose the smoothness scale as we usually do in acquiring the gradients of the approximating image by smoothening it with a single scale. So we proposed a multiscale gradient vector which integrate the different scale gradient vector from coarse image to fine according to our some defined rulers instead of single-scaled gradient vector of images to improve the performance of self-snake model. The efficiency of our proposal was proved by experimental results.
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