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Image denoising study based on balanced multiwavelet

机译:基于平衡多小波的图像去噪研究

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摘要

The particular noise in image not only lower the quality of image but also make denoising more difficult. The multiwavelet with the orthogonality and symmetry decompose the image signal more precisely and denoise better than wavelet. But for multiwavelet transform, the choice of prefilter is extremely important, meanwhile leads to the complication of computation. Furthermore, the image decomposition based on balanced orthogonal multiwavelet is excellent, and does not need pre-filtering. So, in this paper, we propose a method of combining threshold-selecting model with semi-soft thresholding function for image denoising based on the coefficients of balanced orthogonal multiwavelet transform. The simulation results show that this method is superior to others.
机译:图像中的特定噪声不仅降低图像质量,而且使去噪更加困难。具有正交性和对称性的多小波比小波能更精确地分解图像信号,并且去噪效果更好。但是对于多小波变换,预滤波器的选择非常重要,同时导致计算的复杂化。此外,基于平衡正交多小波的图像分解非常出色,并且不需要预滤波。因此,本文提出了一种基于平衡正交多小波变换系数的阈值选择模型与半软阈值函数相结合的图像去噪方法。仿真结果表明,该方法优于其他方法。

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