基于密度聚类与灰度变换的NSST域声呐图像去噪
Sonar Image Denoising Based on Density Clustering and Gray Transformation in NSST Domain
投稿时间:2021-05-31  修订日期:2021-09-15
DOI:
中文关键词:  图像去噪  斑点噪声  非下采样剪切波  密度聚类  灰度变换
英文关键词:image denoising  speckle noise  non-subsampled shearlet  density clustering  gray scale transformation
基金项目:国家自然科学(61761048);云南省地方本科高校基础研究联合专项资金项目[2019FH001(-066)];黑龙江省自然科学基金(LC2018026)
作者单位邮编
刘光宇 大理大学 工程学院 671003
曾志勇 大理大学 工程学院 671003
曹 禹 大理大学 工程学院 
赵恩铭 哈尔滨工程大学 物理与光电工程学院 
邢传玺 云南民族大学 电气信息工程学院 
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中文摘要:
      传统图像去噪方法在去除声呐图像斑点噪声的同时,难以有效保留细节特征。针对该问题,提出了一种基于密度聚类与灰度变换的非下采样剪切波域图像去噪方法。利用非下采样剪切波变换将含噪图像分解为高频系数和低频系数,根据声呐图像中斑点噪声的分布特性,采用DBSCAN密度聚类对高频系数进行处理,分离噪声信号,保留细节信息;对低频系数进行灰度变换,以增强图像对比度。最后,通过非下采剪切波逆变换对处理后的高频系数和低频系数进行重构,实现图像去噪。实验结果表明,该方法在改善图像均方误差、峰值信噪比和结构相似度等指标上效果明显,去噪后图像的视觉效果和边缘保持能力得到较大提升。随着噪声方差的逐渐增大,该方法的优越性得到进一步体现,适用于具有高密度噪声的声呐图像去噪。
英文摘要:
      Traditional image denoising methods are difficult to effectively retain detailed features while filtering the speckle noise of sonar images.To overcome this problem, an image denoising method based on density clustering and gray scale transformation in non-subsampled shearlet domain is proposed. Using non-subsampled shearlet transform to decompose the noisy image into high-frequency coefficients and low-frequency coefficients. According to the distribution characteristics of speckle noise in the sonar image, DBSCAN density clustering is used to process high-frequency coefficients to separate noise interference and retain detailed information ; Carrying out gray scale transformation on low-frequency coefficients to enhance image contrast. Finally, the processed high-frequency coefficients and low-frequency coefficients are reconstructed by non-subsampled shearlet inverse transformation to achieve image denoising. The experimental results show that the method is effective in improving the image's mean square error, peak signal-to-noise ratio, structural similarity and so on. After denoising, the visual effect and edge retention ability of the image are greatly improved. With the gradual increase of the noise variance, the superiority of this method is further manifested, and it is suitable for the denoising of sonar images with high-density noise.
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