A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 14; p. 2336
• Main Authors: Li, Shaobo, Zhao, Jianhu, Zhang, Hongmei, Bi, Zijun, Qu, Siheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2020
• Subjects: Algorithms; Deep water; denoising; Eigenvalues; Filtration; guidance image; Image quality; low-rank; Methods; Noise; Noise reduction; non-local; Researchers; Sediments; Shallow water; Signal to noise ratio; sonar image; Sparsity; sub-bottom profile; Surveying; Ultrasonic imaging; Wavelet transforms; Weight
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12142336

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.