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A New Image Denoising Method Based on Wavelet Multi-scale Registration Fusion

机译:基于小波多尺度配准融合的图像去噪新方法

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Image denoising is an eternal research topic. In this paper, a new image denoising method based on wavelet multiscale registration fusion is proposed to solve the problem that it is easy to lose the edge and texture details of the image in the denoising process. First of all, we can get multiple sets of wavelet coefficients by using different wavelet bases to decompose the same noisy image. Then, the obtained wavelet coefficients are processed by the improved wavelet threshold shrink to get multiple denoising images of the same noisy image. At last, we use the fusion registration algorithm proposed in this paper to fuse the edge feature of multiple denoising images to get the final denoising image. The experiments prove that this method not only can effectively overcome the pseudo gibbs phenomenon caused by the hard threshold method, but also can overcome the image distortion phenomenon caused by the soft threshold method. More importantly, compared with existing methods, this method can effectively preserve the edge detail and texture features of the image and the image has a better visual effect after fusion registration. Therefore, it has a better application value.
机译:图像去噪是永恒的研究课题。提出了一种基于小波多尺度配准融合的图像去噪方法,解决了在去噪过程中容易丢失图像边缘和纹理细节的问题。首先,通过使用不同的小波基分解相同的噪点图像,可以获得多组小波系数。然后,通过改进的小波阈值收缩处理获得的小波系数,以得到相同噪声图像的多个降噪图像。最后,利用本文提出的融合配准算法融合多个降噪图像的边缘特征,得到最终的降噪图像。实验证明,该方法不仅可以有效克服硬阈值法引起的伪吉布斯现象,而且可以克服软阈值法引起的图像失真现象。更重要的是,与现有方法相比,该方法可以有效地保留图像的边缘细节和纹理特征,融合后的图像具有更好的视觉效果。因此,具有较好的应用价值。

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