局部特征分类的自适应扩散模型

Adaptive diffusion model based on Local Feature Classification

  • 摘要: 本文提出了一种既能保持图像特征又能加快扩散速度的自适应扩散模型。首先,该模型利用平均梯度矩阵的特征值区分出图像的平坦区域、线条边缘和角点;其次,该模型根据平均梯度矩阵及其特征向量来控制图像信息的扩散方向;这样,该模型可以在不同的区域自适应地选择不同的扩散方向和扩散速度,从而很好地保持了图像特征并自适应地加快了扩散速度。实验结果表明,该模型可以保持图像结构特征,抑制噪声,应用到数字图像修复中,既能加快图像修复速度,又能使修复后的图像具有更好的视觉效果。

     

    Abstract: In order to not only maintain image features, but also to accelerate diffusion rate, an adaptive diffusion model is proposed in this paper. In this model, the flat areas, line edges and corners of damaged image can be distinguished according to eigenvalues of the average gradient matrix. Meanwhile, diffusion direction of image information is controlled by average gradient matrix and its eigenvectors. Thus, this model can adaptively select diffusion rate and its direction in different regions so as to maintain the structural characteristics of damaged image. The experimental results demonstrate that this model can speed up the diffusion rate while maintaining the image structure. When it is applied in image inpainting, this model can accelerate the inpainting while the inpainted image is in line with human visual effect.

     

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