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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Nonhomogeneous Noise Removal From Side-Scan Sonar Images Using Structural Sparsity
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Nonhomogeneous Noise Removal From Side-Scan Sonar Images Using Structural Sparsity

机译：利用结构稀疏性从侧扫声纳图像中去除非均匀噪声

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摘要

The image quality of side-scan sonar (SSS) is determined by its operating frequency. SSS operating at a low frequency produces low-quality images due to high levels of noise. This noise is randomly generated from a number of different sources, including equipment noise and underwater environmental interference. In addition, to compensate for transmission loss in a received signal, the signal is amplified by time-varied gain correction, and consequently, SSS images contain nonhomogeneous noise, unlike natural images whose noise is assumed to he homogeneous. In this letter, a structural sparsity-based image denoising algorithm is proposed to remove nonhomogeneous noise from SSS images. The algorithm incorporates both local and nonlocal models in the structural features domain in order to guarantee sparsity and enhance nonlocal self-similarity. Using structural features also preserves fine-scale structures, leading to denoised images with natural seabed textures. The patch weights in the nonlocal model are corrected in consideration of the nonhomogeneity of the noise. Experimental results show that the proposed algorithm is qualitatively and quantitatively comparable to conventional algorithms.
机译：侧面扫描声纳（SSS）的图像质量取决于其工作频率。由于噪声水平高，以低频率运行的SSS会产生低质量的图像。这种噪声是从许多不同的源中随机产生的，包括设备噪声和水下环境干扰。另外，为了补偿接收信号中的传输损耗，通过时变增益校正来放大该信号，因此，与假定噪声是均匀的自然图像不同，SSS图像包含非均匀噪声。在这封信中，提出了一种基于结构稀疏性的图像去噪算法，以去除SSS图像中的非均匀噪声。该算法在结构特征域中合并了局部模型和非局部模型，以保证稀疏性并增强非局部自相似性。使用结构特征还可以保留精细的结构，从而获得具有自然海底纹理的去噪图像。考虑到噪声的非均匀性，对非局部模型中的补丁权重进行校正。实验结果表明，该算法在质量和数量上均与常规算法相当。

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