Remove and recover: two stage convolutional autoencoder based sonar image enhancement algorithm

Liu, Ting; Yan, Shun; Wang, Guofeng

doi:10.1007/s11042-023-17673-z

Cited by 1 publication

(2 citation statements)

References 29 publications

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“…The scale decomposer is "maxflat". The orientation is a set of matrices [32,32,16], and the decomposition level is a set of matrices [2,3,4]. Furthermore, Table 1 displays the parameter selection of the low and high-frequency improvement techniques.…”

Section: Data Description and Parameter Settingsmentioning

confidence: 99%

“…Kim et al processed several continuous sonar images using a denoising autoencoder (DAE) technique in order to address the noise correction of the original sonar data [15]. Moreover, Liu et al presented a two-stage convolutional autoencoder (TCAE) method that allows a low-frequency sonar image to reach a resolution comparable to that of high-frequency sonar images [16]. Thomas et al investigated the generation of a set of super-resolution sonar images through the application of generative adversarial networks (GAN) and transfer learning, successfully integrating and correcting multi-source sonar images [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Zhou,

Chen,

Tang

et al. 2024

Remote Sensing

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Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

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