| 摘要: |
| 离散小波变换（Discrete Wavelet Transform，DWT）通常用于图像的表示。然而，对于具有不规则形状边缘的图像，尤其是对于纹理和细节信息较多的遥感图像，DWT却很难有效表示，进而影响后续去噪效果。针对该问题，提出了一种基于图形小波变换（Graphic Wavelet Transform，GWT）的图像去噪方法。首先，将图像表示为图形信号，并通过该图形信号的谱表示构造相应的变换矩阵；然后，设计了一种改进自适应阈值的图像去噪方法，在GWT变换域内对图像去噪。实验结果表明，与常用的图像去噪方法相比，所提算法能够提供更好的图像主观质量。采用均方根误差（Root Mean Square Error，RMSE）和峰值信噪比（Peak Signal-to-Noise Ratio，PSNR）作为客观指标，结果表明，采用所提方法得到的重建图像客观质量更优。 |
| 关键词: 遥感图像 图像表示 图像去噪 图形小波变换 |
| DOI： |
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| 基金项目:国家自然科学基金青年科学基金（41701479）；黑龙江省科学基金项目（QC2018045）；中国博士后科学基金项目（2017M621246）;黑龙江省博士后科学基金项目（LBH-Z17052）；黑龙江省省属高等学校基本科研业务费科研项目（135309342）；2018年国家级大学生创新创业训练计划资助项目（201810232018） |
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| Representation and denoising of remote sensing image based on graph wavelet transform |
| SHI Cuiping,HAN Chongbin,DENG Qiangqiang,CHEN Yang |
| (1.College of Communication and Electronic Engineering,Qiqihar University,Qiqihar 161006,China;2.College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China) |
| Abstract: |
| Discrete wavelet transform(DWT) is usually used for image representation.However,for images with irregular shape edges,especially for remote sensing images with more texture and detail information,DWT is difficult to effectively represent,which affects subsequent applications.To solve this problem,an image denoising method based on graphic wavelet transform(GWT) is proposed.Firstly,the image is represented as a graphic signal,and the corresponding transformation matrix is constructed by the spectral representation of the graphic signal.Then,an improved adaptive threshold image denoising method is designed to denoise the image in the GWT transform domain.The experimental results show that the proposed algorithm can provide better subjective quality of reconstructed images than that of the commonly used image denoising methods.Root mean square error(RMSE) and peak signal-to-noise ratio(PSNR) are used as objective indicators.The results show that the objective quality of the reconstructed image obtained by the proposed method is better. |
| Key words: remote sensing image image representation image denoising graphic wavelet transform |
《电讯技术》编辑部 