传统去噪方法在去除声呐图像斑点噪声的同时，难以拥有很好的细节信息。为此，提出一种基于改进Bregman TV与数学形态学的NSCT声呐图像融合去噪技术。引入图像熵、梯度和边缘强度对Bregman TV的正则参数进行改进，在去噪过程中拥有更多的边缘细节信息。利用新的Bregman TV和数学形态学分别对声呐图像去噪，然后使用NSCT分解为高频和低频，高频拥有大量的边缘信息，低频具有图像细节信息。Bregman TV拥有很好的保边性，数学形态学拥有很好的去噪效果，将2种优势结合，因此采用Bregman TV的高频和数学形态学的低频进行NSCT逆变换，实现图像去噪。实验结果表明，该方法相比于使用基于小波变换和全变分的图像去噪、传统的Bregman TV去噪、数学形态学去噪，更能有效地降低斑点噪声，保留更多的图像细节信息。
Traditional denoising methods are difficult to obtain good detail information while removing speckle noise from sonar images. To solve this problem, a NSCT sonar image fusion denoising technique based on improved Bregman TV and mathematical morphology is proposed. The regularization parameters of Bregman TV were improved by introducing image entropy, gradient and edge intensity, and more edge details were obtained in the denoising process. The sonar image is denoised by the new Bregman TV and mathematical morphology, and then decomposed into high frequency and low frequency by NSCT. The high frequency has a lot of edge information. Low frequency has image detail information. Bregman TV has good edge preservation and mathematical morphology has good denoising effect. Combining the two advantages, the NSCT inverse transformation is carried out by using the high frequency of Bregman TV and the low frequency of mathematical morphology to achieve image denoising. Experimental results show that the proposed method can effectively reduce speckle noise and retain more details of the image, compared with image denoising based on wavelet transform and total variation, traditional Bregman TV denoising and mathematical morphology denoising.
2023,45(15): 97-101 收稿日期：2022-07-20
DOI：10.3404/j.issn.1672-7649.2023.15.018
分类号：TP391
基金项目：国家自然科学基金资助项目（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资助项目（LC2018026）；云南省地方本科高校基础研究联合专项资金项目(202101BA070001-054)
作者简介：刘彪(1998-),男,硕士研究生,研究方向为水下图像处理
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