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Adaptive image denoising and edge enhancement in scale-space using the wavelet transform

机译:小波变换的尺度空间自适应图像去噪与边缘增强

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This paper proposes a new method for image denoising with edge preservation and enhancement, based on image multi-resolution decomposition by a redundant wavelet transform. At each resolution, the coefficients associated with noise and the coefficients associated with edges are modeled by Gaussians, and a shrinkage function is assembled. The shrinkage functions are combined in consecutive resolutions, and geometric constraints are applied to preserve edges that are not isolated. Within the proposed framework, edge related coefficients may be enhanced and denoised simultaneously. Finally, the inverse wavelet transform is applied to the modified coefficients. This method is adaptive, and performs well for images contaminated by natural and artificial noise.
机译:提出了一种基于冗余小波变换的图像多分辨率分解的边缘保持和增强的图像去噪方法。在每个分辨率下,与噪声相关的系数和与边缘相关的系数都由高斯模型建模,并且装配了收缩函数。收缩函数以连续的分辨率进行组合,并且应用几何约束以保留未隔离的边缘。在提出的框架内,边缘相关系数可以同时被增强和去噪。最后,将逆小波变换应用于修改后的系数。该方法是自适应的,对于自然和人造噪声污染的图像效果很好。

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