基于样本矩阵的表面小波声纳图像去噪方法

Dandan Liu
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引用次数: 1

摘要

结合Surfacelet变换和样本矩阵的优点,提出了一种新的声纳图像去噪方法。曲面小波变换擅长处理多维信号,但不适合直接处理二维图像。本文将基于样本矩阵的噪声图像按一个方向循环旋转,压缩成图像序列,并对图像序列进行Surfacelet变换,分别对系数进行硬阈值去噪,然后在空域内进行线性平均。实验结果表明,去噪后的图像不存在小波的吉布斯现象和缺口效应。该方法不仅能获得明显较好的视觉效果和较高的PSNR值,而且还能保留图像的细节和纹理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Sonar Image Denosing Method of Surfacelet Based on Sample Matrix
A new method for sonar image denoising which colligated the strongpoint of Surfacelet transform and sample matrix was presented. Surfacelet transform is good at multidimensional signal processing, but Surfacelet transform is not adapted to process 2-D image directly. In this paper, a noise images based on sample matrix is cycled spinning at a direction and compressed to image sequence, which is implemented Surfacelet transform and employed the hard thresholding denoising to coefficient respectively and then linear average in airspace. The experimental results indicate that image de-noised has not Gibbs-like phenomena of Wavelet and nick effect. The method not only can get obviously better visual effect and high PSNR value, but also hold detail and texture of image.
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